IM originated in response to questions by students how to use theory in intervention development. We began to examine programmes developed in our own work and to identify general principles and procedures we used in our development, which led to the stepwise process of conducting a needs assessment, creating matrices of change objectives, selecting theory-based intervention methods, and translating these into practical applications, developing the programme, planning for implementation, and planning for evaluation (Bartholomew et al., 1998 (link)). The description, definitions, parameters, and examples of the behaviour change methods are the product of a process of joint conceptual analysis that was repeated over time with the various editions (Bartholomew et al., 2011 , 2016 ; Bartholomew, Parcel, Kok, & Gottlieb, 2001 , 2006 ). Most of our definitions of intervention methods are directly derived from the theory involved as published in textbooks on theories of (health) behaviour and change or in reviews and meta-analyses. Some of our definitions are based on definitions by others (e.g., Abraham & Michie, 2008 (link)). At two moments we involved colleagues in the field of health promotion and health psychology for consensus building, evaluation, and improvement. Preparing the 2011 editions, we sent our definitions to 50 colleagues. About 40 colleagues responded with suggestions and in Bartholomew et al. (2011 ) we reformulated the definitions. For the current paper we adapted some definitions based on another round of comments from 20 of the same 40 colleagues (Kok et al., 2012 (link)). We do not presume to give the only possible definition; in our consensus studies we noticed that definitions differ among experts. Together with the definitions we provide the parameters for use: the conditions under which the theory-based method will be effective. We also adapted the parameters, based on recent meta-analyses and reviews (e.g. Peters et al., 2013 (link)). These lists of methods and parameters are not to be used as a cook book. The list is meant to stimulate programme planners to judge the evidence to support the potential of the methods on the list to produce change as well as to assure that the parameters, within which particular methods can be expected to work, are considered.
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Needs Assessment
Needs Assessment
Needs assessment is the process of determining the gaps between current conditions and desired conditions or 'wants'.
It involves systematically gathering and analyzing information to determine the nature and extent of needs, their causes, and the resources required to meet those needs.
Effective needs assessment can streamline research, locate relevant protocols, and leverage AI-driven comparisons to identify the best approaches for enhanced reproducibility and accuracy.
This smotther research process helps optimize outcomes and improve the overall research experience.
It involves systematically gathering and analyzing information to determine the nature and extent of needs, their causes, and the resources required to meet those needs.
Effective needs assessment can streamline research, locate relevant protocols, and leverage AI-driven comparisons to identify the best approaches for enhanced reproducibility and accuracy.
This smotther research process helps optimize outcomes and improve the overall research experience.
Most cited protocols related to «Needs Assessment»
This section provides an overview of selected methods for use of expert judgement in uncertainty analysis. Details of selected methods are reviewed in Section 11.3 and Annexes B.8 and B.9 .
All scientific assessment involves the use of expert judgement (Section5.9 ). The Scientific Committee stresses that where suitable data are available, this should be used in preference to relying solely on expert judgement. When data are strong, uncertainty may be quantified by statistical analysis, and any additional extrapolation or uncertainty addressed by ‘minimal assessment’ (EFSA, 2014a ), or collectively as part of the assessment of overall uncertainty (Section 14 ). When data are weak or diverse, it may be better to quantify uncertainty by expert judgement, supported by consideration of the data.
Expert judgement is subject to a variety of psychological biases (Section5.9 ). Formal approaches for ‘expert knowledge elicitation’ (EKE) have been developed to counter these biases and to manage the sharing and aggregation of judgements between experts. EFSA has published guidance on the application of these approaches to eliciting judgements for quantitative parameters (EFSA, 2014a). Some parts of EFSA's guidance, such as the approaches to identification and selection of experts, are also applicable to qualitative elicitation, but other parts including the detailed elicitation protocols are not. Methods have been described for the use of structured workshops to elicit qualitative judgements in the NUSAP approach (e.g. van der Sluijs et al., 2005 and 2005 ; Bouwknegt and Havelaar, 2015 ) and these could also be adapted for use with other qualitative methods.
The detailed protocols in EFSA (2014a ) can be applied to judgements about uncertain variables, as well as parameters, if the questions are framed appropriately (e.g. eliciting judgements on the median and the ratio of a higher quantile to the median). EFSA (2014a ) does not address other types of judgements needed in EFSA assessments, including prioritising uncertainties and judgements about dependencies, model uncertainty, categorical questions, approximate probabilities or probability bounds. More guidance on these topics, and on the elicitation of uncertain variables, would be desirable in future.
Formal elicitation requires significant time and resources, so it is not feasible to apply it to every source of uncertainty affecting an assessment. This is recognised in the EFSA (2014a ) guidance, which includes an approach for prioritising parameters for formal EKE and ‘minimal assessment’ for more approximate elicitation of less important parameters. Therefore, in the present guidance, the Scientific Committee describes an additional, intermediate process for ‘semi‐formal’ expert elicitation (Section 11.3.1 and Annex B.8 ).
It is important also to recognise that generally, further scientific judgements will be made, usually by a Working Group of experts preparing the assessment: these are referred to in this document as judgements by‘expert group judgement’ . Normal Working Group procedures include formal processes for selecting relevant experts, and for the conduct, recording and review of discussions. These processes address some of the principles for EKE. Chairs of Working Groups should be aware of the potential for psychological biases, mentioned above, and seek to mitigate them when managing the discussion (e.g. discuss ranges before central estimates, encourage consideration of alternative views).
In practice, there is not a dichotomy between more and less formal approaches to EKE, but rather a continuum. Individual EKE exercises should be conducted at the level of formality appropriate to the needs of the assessment, considering the importance of the assessment, the potential impact of the uncertainty on decision‐making, and the time and resources available.
All scientific assessment involves the use of expert judgement (Section
Expert judgement is subject to a variety of psychological biases (Section
The detailed protocols in EFSA (
Formal elicitation requires significant time and resources, so it is not feasible to apply it to every source of uncertainty affecting an assessment. This is recognised in the EFSA (
It is important also to recognise that generally, further scientific judgements will be made, usually by a Working Group of experts preparing the assessment: these are referred to in this document as judgements by
In practice, there is not a dichotomy between more and less formal approaches to EKE, but rather a continuum. Individual EKE exercises should be conducted at the level of formality appropriate to the needs of the assessment, considering the importance of the assessment, the potential impact of the uncertainty on decision‐making, and the time and resources available.
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Debility
Needs Assessment
Workshops
This section provides an overview of selected methods for use of expert judgement in uncertainty analysis. Details of selected methods are reviewed in Section 11.3 and Annexes B.8 and B.9 .
All scientific assessment involves the use of expert judgement (Section5.9 ). The Scientific Committee stresses that where suitable data are available, this should be used in preference to relying solely on expert judgement. When data are strong, uncertainty may be quantified by statistical analysis, and any additional extrapolation or uncertainty addressed by ‘minimal assessment’ (EFSA, 2014a (link)), or collectively as part of the assessment of overall uncertainty (Section 14 ). When data are weak or diverse, it may be better to quantify uncertainty by expert judgement, supported by consideration of the data.
Expert judgement is subject to a variety of psychological biases (Section5.9 ). Formal approaches for ‘expert knowledge elicitation’ (EKE) have been developed to counter these biases and to manage the sharing and aggregation of judgements between experts. EFSA has published guidance on the application of these approaches to eliciting judgements for quantitative parameters (EFSA, 2014a). Some parts of EFSA's guidance, such as the approaches to identification and selection of experts, are also applicable to qualitative elicitation, but other parts including the detailed elicitation protocols are not. Methods have been described for the use of structured workshops to elicit qualitative judgements in the NUSAP approach (e.g. van der Sluijs et al., 2005 (link) and 2005 (link); Bouwknegt and Havelaar, 2015 ) and these could also be adapted for use with other qualitative methods.
The detailed protocols in EFSA (2014a (link)) can be applied to judgements about uncertain variables, as well as parameters, if the questions are framed appropriately (e.g. eliciting judgements on the median and the ratio of a higher quantile to the median). EFSA (2014a (link)) does not address other types of judgements needed in EFSA assessments, including prioritising uncertainties and judgements about dependencies, model uncertainty, categorical questions, approximate probabilities or probability bounds. More guidance on these topics, and on the elicitation of uncertain variables, would be desirable in future.
Formal elicitation requires significant time and resources, so it is not feasible to apply it to every source of uncertainty affecting an assessment. This is recognised in the EFSA (2014a (link)) guidance, which includes an approach for prioritising parameters for formal EKE and ‘minimal assessment’ for more approximate elicitation of less important parameters. Therefore, in the present guidance, the Scientific Committee describes an additional, intermediate process for‘semi‐formal’ expert elicitation (Section 11.3.1 and Annex B.8 ).
It is important also to recognise that generally, further scientific judgements will be made, usually by a Working Group of experts preparing the assessment: these are referred to in this document as judgements by‘expert group judgement’ . Normal Working Group procedures include formal processes for selecting relevant experts, and for the conduct, recording and review of discussions. These processes address some of the principles for EKE. Chairs of Working Groups should be aware of the potential for psychological biases, mentioned above, and seek to mitigate them when managing the discussion (e.g. discuss ranges before central estimates, encourage consideration of alternative views).
In practice, there is not a dichotomy between more and less formal approaches to EKE, but rather a continuum. Individual EKE exercises should be conducted at the level of formality appropriate to the needs of the assessment, considering the importance of the assessment, the potential impact of the uncertainty on decision‐making, and the time and resources available.
All scientific assessment involves the use of expert judgement (Section
Expert judgement is subject to a variety of psychological biases (Section
The detailed protocols in EFSA (2014a (link)) can be applied to judgements about uncertain variables, as well as parameters, if the questions are framed appropriately (e.g. eliciting judgements on the median and the ratio of a higher quantile to the median). EFSA (2014a (link)) does not address other types of judgements needed in EFSA assessments, including prioritising uncertainties and judgements about dependencies, model uncertainty, categorical questions, approximate probabilities or probability bounds. More guidance on these topics, and on the elicitation of uncertain variables, would be desirable in future.
Formal elicitation requires significant time and resources, so it is not feasible to apply it to every source of uncertainty affecting an assessment. This is recognised in the EFSA (2014a (link)) guidance, which includes an approach for prioritising parameters for formal EKE and ‘minimal assessment’ for more approximate elicitation of less important parameters. Therefore, in the present guidance, the Scientific Committee describes an additional, intermediate process for
It is important also to recognise that generally, further scientific judgements will be made, usually by a Working Group of experts preparing the assessment: these are referred to in this document as judgements by
In practice, there is not a dichotomy between more and less formal approaches to EKE, but rather a continuum. Individual EKE exercises should be conducted at the level of formality appropriate to the needs of the assessment, considering the importance of the assessment, the potential impact of the uncertainty on decision‐making, and the time and resources available.
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Debility
Needs Assessment
Workshops
The data for assessing the level of collaboration between LHDs and nonprofit hospitals came from the 2015 Forces of Change Survey administered by the National Association of County and City Health Officials (NACCHO). This survey was developed to measure the impacts of economic forces on the budget, staff, and programs of LHDs. The survey was administered to a subset of the nearly 3000 LHDs across the country using stratified random sampling (on state and size of population in the LHD jurisdiction) [21 ]. Nine hundred and forty-eight (948) LHDs were randomly selected, of which 690 LHDs participated (73% response rate). Approximately 77% of the included LHDs reported having at least one nonprofit hospital in their jurisdiction (n = 519).
We used each LHD’s response to a single question as a proxy for whether it had a long-standing collaboration with nonprofit hospitals in the community for which the LHD was responsible [22 , 23 (link)]. The survey question asks “Is your LHD included in any nonprofit hospital’s implementation plan for the community health needs assessment (CHNA)?” Response options included: no collaboration, participating in the development of a hospital implementation plan, listed as a partner in a hospital implementation plan, conducting an activity together in a hospital implementation plan, and using the same implementation plan as the hospital. Because we were interested in identifying established collaborations between LHDs and hospitals within local communities, we created a binary variable indicating “long-standing collaboration” for those LHDs that reported conducting an activity together or using the same implementation plan as the nonprofit hospital in their community. Although the survey question did not specify a defined time period for reported LHD-hospital collaboration, such CHNA implementation efforts typically entail multiple years of activity. Accordingly, we interpreted LHD responses indicating a joint effort for implementing community health needs assessments to be reflective of relatively long-standing relationships (or lack thereof) between an LHD and one or more nonprofit hospitals in a community. While this variable lacked granularity in terms of the nature, strength, and scale of LHD-hospital collaboration (e.g., the content of implementation plans was not known), previous research suggests that any level of meaningful, ongoing collaboration between these two sectors within the same community is uncommon [24 (link)]. Thus, we constructed this variable to measure if such collaboration is associated with positive individual-level health outcomes.
We used each LHD’s response to a single question as a proxy for whether it had a long-standing collaboration with nonprofit hospitals in the community for which the LHD was responsible [22 , 23 (link)]. The survey question asks “Is your LHD included in any nonprofit hospital’s implementation plan for the community health needs assessment (CHNA)?” Response options included: no collaboration, participating in the development of a hospital implementation plan, listed as a partner in a hospital implementation plan, conducting an activity together in a hospital implementation plan, and using the same implementation plan as the hospital. Because we were interested in identifying established collaborations between LHDs and hospitals within local communities, we created a binary variable indicating “long-standing collaboration” for those LHDs that reported conducting an activity together or using the same implementation plan as the nonprofit hospital in their community. Although the survey question did not specify a defined time period for reported LHD-hospital collaboration, such CHNA implementation efforts typically entail multiple years of activity. Accordingly, we interpreted LHD responses indicating a joint effort for implementing community health needs assessments to be reflective of relatively long-standing relationships (or lack thereof) between an LHD and one or more nonprofit hospitals in a community. While this variable lacked granularity in terms of the nature, strength, and scale of LHD-hospital collaboration (e.g., the content of implementation plans was not known), previous research suggests that any level of meaningful, ongoing collaboration between these two sectors within the same community is uncommon [24 (link)]. Thus, we constructed this variable to measure if such collaboration is associated with positive individual-level health outcomes.
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Cytoplasmic Granules
Hospital Planning
Infantile Neuroaxonal Dystrophy
Joints
Needs Assessment
The guideline was developed according to a well-documented methodology that is universal to ESHRE guidelines (Vermeulen, 2014 ).
In short, 18 key questions were formulated by the Guideline Development Group (GDG), with input from patient organizations (Fertility Europe, Miscarriage Association UK), and structured in PICO format (Patient, Intervention, Comparison, Outcome). For each question, databases (PUBMED/MEDLINE and the Cochrane library) were searched from inception to 31 March 2017, with a limitation to studies written in English. From the literature searches, studies were selected based on the PICO questions, assessed for quality and summarized in evidence tables and summary of findings tables (for interventions with at least two studies per outcome). Cumulative live birth rate, live birth rate and pregnancy loss rate (or miscarriage rate) were considered the critical outcomes. GDG meetings were organized where the evidence and draft recommendations were presented by the assigned GDG member, and discussed until consensus was reached within the group.
Each recommendation was labelled as strong or conditional and a grade was assigned based on the strength of the supporting evidence (High ⊕⊕⊕⊕ – Moderate ⊕⊕⊕○ Low ⊕⊕○○ – Very low ⊕○○○). In the absence of evidence, the GDG formulated no recommendation or a good practice points (GPP) based on clinical expertise (TableI ).
www.eshre.eu/guidelines ).
This guideline will be considered for update 4 years after publication, with an intermediate assessment of the need for updating 2 years after publication.
In short, 18 key questions were formulated by the Guideline Development Group (GDG), with input from patient organizations (Fertility Europe, Miscarriage Association UK), and structured in PICO format (Patient, Intervention, Comparison, Outcome). For each question, databases (PUBMED/MEDLINE and the Cochrane library) were searched from inception to 31 March 2017, with a limitation to studies written in English. From the literature searches, studies were selected based on the PICO questions, assessed for quality and summarized in evidence tables and summary of findings tables (for interventions with at least two studies per outcome). Cumulative live birth rate, live birth rate and pregnancy loss rate (or miscarriage rate) were considered the critical outcomes. GDG meetings were organized where the evidence and draft recommendations were presented by the assigned GDG member, and discussed until consensus was reached within the group.
Each recommendation was labelled as strong or conditional and a grade was assigned based on the strength of the supporting evidence (High ⊕⊕⊕⊕ – Moderate ⊕⊕⊕○ Low ⊕⊕○○ – Very low ⊕○○○). In the absence of evidence, the GDG formulated no recommendation or a good practice points (GPP) based on clinical expertise (Table
Interpretation of strong versus conditional recommendations in the GRADE approach.
| Implications for | Strong recommendation | Conditional recommendation |
|---|---|---|
| Patients | Most individuals in this situation would want the recommended course of action, and only a small proportion would not. | The majority of individuals in this situation would want the suggested course of action, but many would not. |
| Clinicians | Most individuals should receive the intervention. Adherence to this recommendation according to the guideline could be used as a quality criterion or performance indicator. Formal decision aids are not likely to be needed to help individuals make decisions consistent with their values and preferences. | Recognize that different choices will be appropriate for individual patients and that you must help each patient arrive at a management decision consistent with his or her values and preferences. Decision aids may be useful in helping individuals to make decisions consistent with their values and preferences. |
| Policy makers | The recommendation can be adopted as policy in most situations. | Policy making will require substantial debate and involvement of various stakeholders. |
*Andrews et al. (2013) (link).
This guideline will be considered for update 4 years after publication, with an intermediate assessment of the need for updating 2 years after publication.
cDNA Library
Endoscopy, Gastrointestinal
Europeans
Fertility
Gynecologist
Needs Assessment
Obstetrician
Patients
Spontaneous Abortion
Uterine Anomalies
Most recents protocols related to «Needs Assessment»
Example 5
In this Example, the lung metastasis-suppressing effects of anti-S100A8/A9 monoclonal antibodies were investigated. Through use of a lung metastasis model of human breast cancer MDA-MB-231 cells, the lung metastasis-suppressing effects of anti-S100A8/A9 monoclonal antibodies were investigated. For the MDA-MB-231 cells, a line stably expressing GFP was generated.
In accordance with a protocol illustrated in
As a result, it was recognized that Clone Nos. 85, 258, and 260 showed significant lung metastasis-suppressing effects. For the MDA-MB-231 cells, mouse lung metastasis was hardly found, suggesting a need for a further investigation on the generation of a metastasis model.
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Breast
Breast Carcinoma
Clone Cells
Homo sapiens
Lung
Lung Cancer
MDA-MB-231 Cells
Mice, Inbred BALB C
Mice, Nude
Monoclonal Antibodies
Mus
Needs Assessment
Neoplasm Metastasis
Neoplasms
Tail
Veins
Veins, Pulmonary
X-Ray Computed Tomography
This is a mixed-methods study to evaluate IDDEAS, a decision support system for diagnosis and treatment of children and adolescents in Norwegian CAMHS. The IDDEAS project is organized into the following stages: (1) The Assessment of Needs and Preparation of IDDEAS; (2) The Development of the IDDEAS CDSS model; (3) The Evaluation of the IDDEAS CDSS; and (4) Implementation and Dissemination (see Figure 1 ). This qualitative study reports on the interviews conducted as one component of the usability evaluation of the first IDDEAS prototype (11 (link)).
This evaluation process utilizes user-centered design (UCD) methods, with the testing of the CDSS conducted in phases of developmental iterations. The UCD methods include formative usability sessions (12 (link), 31 (link)), cognitive walk-through/think-aloud procedures (5 (link), 32 (link)), iterative development with end-users, and utilization of both qualitative and quantitative methods of inquiry (31 (link), 33 ). As part of UCD, the iterative development of the CDSS involves continuous collaboration with CAMHS clinicians. The specific methods and the development plan are detailed in the IDDEAS project protocol (11 (link)). The present study serves as the first usability test, using UCD methods to investigate Norwegian CAMHS clinicians’ perceptions of the usability, utility, and overall functionality of the IDDEAS prototype.
This evaluation process utilizes user-centered design (UCD) methods, with the testing of the CDSS conducted in phases of developmental iterations. The UCD methods include formative usability sessions (12 (link), 31 (link)), cognitive walk-through/think-aloud procedures (5 (link), 32 (link)), iterative development with end-users, and utilization of both qualitative and quantitative methods of inquiry (31 (link), 33 ). As part of UCD, the iterative development of the CDSS involves continuous collaboration with CAMHS clinicians. The specific methods and the development plan are detailed in the IDDEAS project protocol (11 (link)). The present study serves as the first usability test, using UCD methods to investigate Norwegian CAMHS clinicians’ perceptions of the usability, utility, and overall functionality of the IDDEAS prototype.
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Adolescent
Child
Cognition
Diagnosis
Needs Assessment
Process Assessment, Health Care
Triglyceride Storage Disease with Ichthyosis
Physical activity, sleep, and circadian rhythm were measured by actigraphy using the MotionWatch8 (CamNtech, Ltd., Cambridge, United Kingdom), a wrist-worn device that contains a tri-axial accelerometer that is validated for measuring physical activity and sleep in clinical and non-clinical populations (42 (link), 43 ). Participants were instructed to wear the device 24 h a day for 7 days and 14 h (the maximum recording length of the device) continuously on the wrist of the non-dominant arm. The length of this observation period was chosen because the current requirements for actigraphy for clinical purposes require recording of at least 72 h and extended monitoring (5 days or longer) reduces the inherent measurement errors in actigraphy and increases reliability (13 (link), 44 (link)). Also, capturing both weekdays and weekend days can result in a more complete clinical picture (44 (link)). To facilitate sleep and circadian rhythm analysis, participants were instructed to press an event marker before they went to sleep and when they got up. Data were stored at 5-s intervals, to collect almost continuous data needed for the evaluation of movement patterns. Data were analyzed within the proprietary software package (Motionware V1.1.25, CamNtech).
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Actigraphy
Circadian Rhythms
Medical Devices
Movement
Needs Assessment
Population Group
Sleep
Wrist
Depressive symptoms were observed prior to and during the pandemic using monthly data from beginning of April 2019 until mid-June 2022 (see Figure 1 ). There were four short data gaps (the largest between January and mid-March 2021). The indicator was measured with the established ultra-brief screening instrument “Patient Health Questionnaire-2” (PHQ-2) (86 (link)), which has been found to perform well as a screening tool for depressive disorders in the German general population (92 (link)). The PHQ-2 captures the frequencies of two core symptoms of depressive disorders, asking, “Over the last 2 weeks, how often have you been bothered by the following problems?”: (1) “little interest and pleasure in doing things” (2) “feeling down, depressed or hopeless” (possible responses: 0 = “not at all,” 1 = “several days,” 2 = “more than half the days,” 3 = “nearly every day”). The total score of the PHQ-2 ranges from 0 to 6 (“no symptoms” to “severe symptoms”). According to scoring recommendations (92 (link)), scores ≥ 3 represent a positive screen for possible depressive disorder and indicate a potential need for further diagnostic assessment. In our analytical sample, the internal consistency of the PHQ-2 is α = 0.73 [standardized alpha coefficient as recommended for two items (93 (link)), unstandardized α = 0.72], slightly higher than in a comparable German sample (45 (link)). Two measures are reported in the current study: (1) the mean depressive symptom score, which tracks changes in the mean severity of symptoms in the population (73 (link)); (2) the proportion of the adult population screening positive for possible depressive disorder.
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Adult
Depressive Symptoms
Diagnosis
Disorder, Depressive
Needs Assessment
Pandemics
Pleasure
One of the most widely used methods to investigate the effect of economic activities on environmental pollution is the EKC framework. Empirical examinations of the determinants of carbon emissions extensively utilise the EKC framework (Zhang 2010 ; Wang et al. 2023a (link)). The EKC framework, more acceptable in the case of panel data analysis (Akram et al. 2020 ), indicates an inverted U-shaped curve: initially, economic growth increases environmental pollution, but, after peaking, environmental pollution decreases with increase in economic growth (Wang et al. 2023b ).
In an empirical setting, modelling the impacts of ENEF on CAE requires a well-established econometric approach that can be augmented according to the need of this study. The energy efficiency–augmented EKC hypothesis, proposed by Stern (2004 ) and Mahapatra and Irfan (2021 ), investigates the effect of economic growth on CAE by controlling the influence of ENEF in the model. Modelling the effects of ENEF on CAE in an empirical setting requires a proven econometric approach that can be supplemented as needed for this investigation. The energy efficiency–augmented EKC hypothesis, proposed by Stern (2004 ) and Mahapatra and Irfan (2021 ), examines the impact of economic growth on CAE by controlling the influence of ENEF in the model. The EKC model is regularly modified with a wide range of other factors to assess their empirical relationship with carbon emissions (Shahbaz 2018 ; Irfan et al. 2021 ; Wang et al. 2023c (link)). As per the objective of this study, we formulated the following econometric model based on the ENEF–augmented EKC framework: where prefix ln represents natural logarithmic transformation of the variable, lnCAE denotes the level of carbon emissions, lnY refers to real value added, lnY2 denotes square term of the real value added, and lnENEF denote energy efficiency.
The coefficients and measure the impact of real value added and the square term of the real value added on CAE. The coefficient measures the effect of ENEF on CAE. The positive and statistically significant coefficient for ENEF represents a positive effect of ENEF on CAE: an increase in ENEF will result in a rise in CAE (Akram et al. 2020 ; Das and Roy 2020 ; Javid and Khan 2020 ). However, the negative and statistically significant coefficient for ENEF denotes a negative impact of ENEF on CAE, which suggests that a rise in ENEF will lead to a rise in CAE (Mahapatra and Irfan 2021 ). One of the crucial advantages of a rise in ENEF is that it diminishes the energy consumption to generate same volume of output (Wei et al. 2010 ; Cambridge Econometrics 2015 ). Since CAE is positively associated with the consumption of energy, especially fossil fuel–based energy, a reduction in consumption will lead to a fall in CAE (Gunatilake et al. 2014 ; Wang and Wang 2020 ). However, this mechanism is sometimes upset by the rebound effect (Gillingham et al. 2020 )—an increase in ENEF contributes lesser reduction in energy consumption—because it can lead to a net rise in energy consumption overall (Sorrell 2009 ), which can increase CAE.
The model proposed in Eq. (7 ) can present the results for the symmetric effects of ENEF on CAE for India. However, this study is interested in capturing the nonlinear behaviour in ENEF and in examining the asymmetric influence of ENEF on CAE for India. Therefore, the nonlinear specification of the model (as shown in Eq. (7 )) is reformulated in the next section, which can accommodate the asymmetric effect of ENEF in the model.
In an empirical setting, modelling the impacts of ENEF on CAE requires a well-established econometric approach that can be augmented according to the need of this study. The energy efficiency–augmented EKC hypothesis, proposed by Stern (2004 ) and Mahapatra and Irfan (2021 ), investigates the effect of economic growth on CAE by controlling the influence of ENEF in the model. Modelling the effects of ENEF on CAE in an empirical setting requires a proven econometric approach that can be supplemented as needed for this investigation. The energy efficiency–augmented EKC hypothesis, proposed by Stern (2004 ) and Mahapatra and Irfan (2021 ), examines the impact of economic growth on CAE by controlling the influence of ENEF in the model. The EKC model is regularly modified with a wide range of other factors to assess their empirical relationship with carbon emissions (Shahbaz 2018 ; Irfan et al. 2021 ; Wang et al. 2023c (link)). As per the objective of this study, we formulated the following econometric model based on the ENEF–augmented EKC framework: where prefix ln represents natural logarithmic transformation of the variable, lnCAE denotes the level of carbon emissions, lnY refers to real value added, lnY2 denotes square term of the real value added, and lnENEF denote energy efficiency.
The coefficients and measure the impact of real value added and the square term of the real value added on CAE. The coefficient measures the effect of ENEF on CAE. The positive and statistically significant coefficient for ENEF represents a positive effect of ENEF on CAE: an increase in ENEF will result in a rise in CAE (Akram et al. 2020 ; Das and Roy 2020 ; Javid and Khan 2020 ). However, the negative and statistically significant coefficient for ENEF denotes a negative impact of ENEF on CAE, which suggests that a rise in ENEF will lead to a rise in CAE (Mahapatra and Irfan 2021 ). One of the crucial advantages of a rise in ENEF is that it diminishes the energy consumption to generate same volume of output (Wei et al. 2010 ; Cambridge Econometrics 2015 ). Since CAE is positively associated with the consumption of energy, especially fossil fuel–based energy, a reduction in consumption will lead to a fall in CAE (Gunatilake et al. 2014 ; Wang and Wang 2020 ). However, this mechanism is sometimes upset by the rebound effect (Gillingham et al. 2020 )—an increase in ENEF contributes lesser reduction in energy consumption—because it can lead to a net rise in energy consumption overall (Sorrell 2009 ), which can increase CAE.
The model proposed in Eq. (
Carbon
Environmental Pollution
factor A
Fatigue
Needs Assessment
Physical Examination
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The Vivid E95 is a high-performance cardiovascular ultrasound system designed for comprehensive cardiac and vascular imaging. It features advanced technologies and a user-friendly interface.
More about "Needs Assessment"
Needs assessment is a crucial process in research and planning, involving the systematic gathering and analysis of information to determine the gaps between current and desired conditions.
This process helps identify the nature and extent of needs, their underlying causes, and the resources required to address them.
Effective needs assessment can streamline research, locate relevant protocols, and leverage AI-driven comparisons to identify the best approaches for enhanced reproducibility and accuracy.
This smoother research process helps optimize outcomes and improve the overall research experience.
Needs assessment can be applied across various fields, including healthcare, education, social services, and business.
It is often used to assess the needs of a target population, identify unmet needs, and prioritize areas for intervention.
Techniques such as surveys, interviews, focus groups, and data analysis can be employed to gather the necessary information.
The insights gained from needs assessment can inform the selection of appropriate research methodologies, sample size calculations (e.g., using SPSS version 20 or Stata/SE 14.2), and the application of relevant analytical tools (e.g., Prism 8, Whole-Genome 2.7M Array, Masson's trichrome, Discovery bone densitometer, Vivid E95).
By identifying the most relevant protocols and leveraging AI-driven comparisons, researchers can enhance the reproducibility and accuracy of their studies, leading to more impactful and meaningful outcomes.
In summary, needs assessment is a powerful tool that can streamline the research process, optimize outcomes, and improve the overall research experience.
By systematically gathering and analyzing information, researchers can make informed decisions, locate relevant protocols, and leverage cutting-edge technologies to enhance the quality and impact of their work.
This process helps identify the nature and extent of needs, their underlying causes, and the resources required to address them.
Effective needs assessment can streamline research, locate relevant protocols, and leverage AI-driven comparisons to identify the best approaches for enhanced reproducibility and accuracy.
This smoother research process helps optimize outcomes and improve the overall research experience.
Needs assessment can be applied across various fields, including healthcare, education, social services, and business.
It is often used to assess the needs of a target population, identify unmet needs, and prioritize areas for intervention.
Techniques such as surveys, interviews, focus groups, and data analysis can be employed to gather the necessary information.
The insights gained from needs assessment can inform the selection of appropriate research methodologies, sample size calculations (e.g., using SPSS version 20 or Stata/SE 14.2), and the application of relevant analytical tools (e.g., Prism 8, Whole-Genome 2.7M Array, Masson's trichrome, Discovery bone densitometer, Vivid E95).
By identifying the most relevant protocols and leveraging AI-driven comparisons, researchers can enhance the reproducibility and accuracy of their studies, leading to more impactful and meaningful outcomes.
In summary, needs assessment is a powerful tool that can streamline the research process, optimize outcomes, and improve the overall research experience.
By systematically gathering and analyzing information, researchers can make informed decisions, locate relevant protocols, and leverage cutting-edge technologies to enhance the quality and impact of their work.