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Pessimism

Pessimism is a mental state characterized by a tendency to emphasize negative or unfavorable aspects of a situation or outcome.
It involves a general expectation that things will turn out for the worst, or a lack of hope or confidence in the future.
Pessimism can manifest in various ways, such as a persistent focus on difficulties, a belief that problems are insurmountable, or a tendency to anticipate and prepare for the worst.
This mental disposition can have significant impacts on an individual's mood, decision-making, and overall well-being.
Researchers in the field of pessimism studies utilize a variety of methods, including experimental psychology, neuroscience, and data analysis, to better understand the causes, consequences, and potential interventions for pessimistic thinking.
The PubCompare.ai platform offers powerful AI-driven tools to assist in this important area of research, helping scientists easily locate the most effective protocols and solutions to support their pessimism studies.

Most cited protocols related to «Pessimism»

We developed a questionnaire that initially included 79 items assessing each of the domains through their related key constructs (see Additional file
1). Constructs within domains were selected based on conceptual relatedness to the content of the domain (i.e., Knowledge, Procedural knowledge, Skills, Professional role, and Memory); inclusion in relevant theories frequently used in the field of behavior change (and thus ready access to existing items): the Theory of Planned Behavior
[41 (link)] (i.e., Perceived behavioral control, Attitudes, Subjective norm, and Intention) and Social Cognitive Theory
[42 (link)] (i.e., Self-efficacy, Outcome expectancies, and Social support); existence of validated scales (i.e., Optimism, Pessimism, Action planning, Attention, Affect, Stress, Automaticity, and Self-monitoring); and/or relevance to the implementation of PA interventions in routine healthcare by mapping factors resulting from previous research
[43 (link),44 (link)] onto the TDF domains. JP and JMH independently identified that the constructs Reinforcement, Priority, Resources/materials, and Descriptive norm were salient in the previous PA-based research and thus these constructs were also included as construct-indicators of their respective domains.
Items measuring constructs within the domains Knowledge, Beliefs about capabilities, Optimism, Beliefs about consequences, Intentions, Social influences, Emotion, and Behavioral regulation were adapted from previously published questionnaires (i.e.,
[34 (link),35 (link),41 (link),42 (link),45 -53 ]). Given lack of available questionnaires in the literature for some domains, new items were created for the domains Skills, Social/professional role and identity, Reinforcement, and Environmental context and resources. With regard to the domain Goals, items were newly developed for the construct Priority (as none could be located in the literature), while items measuring the construct Action planning were adapted from a previously published questionnaire
[46 (link)]. With regard to the domain Memory, attention, and decision making, items measuring the construct Attention were adapted from a previously published questionnaire
[51 (link)] and items measuring the construct Memory were newly developed. New items were developed based on discussions between JP and JMH. These discussions were informed by the academic literature on the concept and definition of specific domains and constructs, questions to identify behavior change processes as formulated by Michie et al.[31 (link)], and themes emerging from interviews on the implementation of PA interventions
[43 (link)]. WAG and MRC supervised the development of the questionnaire and reviewed items’ face validity.
To develop a questionnaire which could be used by researchers in different fields of implementation research, items were formulated in a generic way using a '[action] in [context, time] with [target]’ construction based on the 'TACT principle’
[38 ], whereby researchers can specify the target, action, context, and time relevant to their research. The questionnaire was developed in English, then translated to Dutch and back-translated to English by an independent translator. The small amount of differences between the original and back-translated version of the questionnaire were discussed and adaptations were made.
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Publication 2014
Acclimatization Attention Behavior Control bis(tetraheptylammonium)tetraiodocyclopentane tellurate(IV) Emotions Generic Drugs Memory Optimism Pessimism Reinforcement, Psychological

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Publication 2011
Anxiety Back Pain Disabled Persons Fear Patients Pessimism Physical Examination Physical Therapist Psychometrics Respiratory Diaphragm Satisfaction Severity, Pain Therapy, Physical
For each simulation, we calculated the following measures for goodness-of-fit:
R2, using the most general definition [5 (link),8 (link)]:
Formula 10:
with RSS = residual sum-of-squares, TSS = total sum-of-squares, y = response values, = fitted values and = the mean of response values. For a more detailed description see Remarks 1-6 in Additional File 1.
We chose to use the adjusted R2 to compensate for possible bias due to different number of parameters:
Formula 11:
with n = sample size and p = number of parameters.
The Akaike Information Criterion (AIC, [10 -12 (link)]), a measure that is widely accepted for measuring the validity within a cohort of nonlinear models and frequently used for model selection [13 ].
Formula 12:
with p = number of parameters and ln(L) = maximum log-likelihood of the estimated model. The latter, in the case of a nonlinear fit with normally distributed errors [13 ], is calculated by
Formula 13:
with x1, ..., xn = the residuals from the nonlinear least-squares fit and N = their number.
To provide a fair playing ground, we employed an AIC variant that corrects for small sample sizes, the bias-corrected AIC (AICc):
Formula 14:
with n = sample size and p = number of parameters.
In order to obtain values for the validity of a fit, we used Akaike weights which calculate the weight of evidence for each model within a cohort of models in question [12 (link)-14 ]:
Formula 15:
with i, k = model numbers, Δi(AIC) = the difference in AIC of each model in comparison to the model with the lowest AIC, subsequently normalized to their sum (denominator).
Also here, we used the bias-corrected AICc for calculating the Akaike weights.
We also chose to employ the Bayesian Information Criterion (BIC), which gives a higher penalty on the number of parameters [15 (link)]:
Formula 16:
with p = number of parameters, n = sample size and L = maximum likelihood of the estimated model.
Furthermore, the residual variance as the part of the variance that cannot be accounted for by the model:
Formula 17:
with RSS = residual sum-of-squares, n = sample size and p = number of parameters.
The variance of a least-squares fit is also characterized by the chi-square statistic defined as Formula 18:
where yi = response values, f(xi) = the fitted values and = the uncertainty in the individual measurements yi. We further define the reduced chi-square as a useful measure [16 ] by
Formula 19:
with ν = n - p (degrees of freedom). If the fitting function is a good approximation to the parent function, then the variances of both should agree well, and the reduced chi-square should be approximately unity. If the reduced chi-square is much larger than 1 (i.e. 10 or 100), it means that one is either overly optimistic about the measurement errors or that one selected an inappropriate fitting function. If reduced chi-square is too small (i.e. 0.1 or 0.01) it may mean that one has been too pessimistic about measurement errors. For this work, models were selected based on reduced chi-square by being closest to 1.
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Publication 2010
Optimism Parent Pessimism

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Publication 2017
Alcoholic Beverages Optimism Pessimism Tobacco Products
We assigned the five main IUCN risk levels to the tips of 100 100-species birth death trees (b = 0.1, d = 0.06), in the same proportion per level as mean for the birds and mammals of the world [12] , [13] ; see our Table 1. We then converted each species' level to a probability of extinction under each of five transformations: one where each increase in level corresponds to a doubling of extinction risk [12] three transformations corresponding to the official IUCN designations, but scaled to 50, 100, and 500 year windows, and a pessimistic transformation of our choosing. The IUCN has not designated prob(extinction) for the two lowest categories, and these had to be interpolated. Partly in order to produce contrasting scales, we set prob(extinction) for the ‘least concern’ species to 0.01% [13] , equivalent to assuming that at most 1 of the 7600 bird species in this category would go extinct over the next 100 years; the Near Threatened category was given a prob(extinction) 100 times this, in accord with the interpolation used in [13] .
For each tree and assignment, we calculated the EDGE and HEDGE scores using the Tuatara module [26] of the Mesquite package [27] . We asked how often the top ranked species differed as one moved between transformations. When the ranks differed between transformations, we also recorded the degree of this difference by taking the sum of the differences in ranks. For example, if the top five species under the Isaac transformation {1,2,3,4,5} are ranked {1,5,3,10,2} under the IUCN100 transformation, this contributes 12 (0+3+0+6+3) to the sum, and if the top five species under the IUCN100 transformation {1,2,3,4,5} are ranked {1,5,3,8,2} under the Isaac et al. transformation, this contributes 10 {0+3+0+4+3}, giving a summed difference score of 22. We considered four measures of sensitivity to transformation. For the top five- and for the top 20-ranked species under a transformation, we recorded the proportion of the simulated trees that showed any difference, and also the average across trees of the sum of these differences in ranks.
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Publication 2008
Aves Birth Extinction, Psychological Extinction, Species Hypersensitivity Mammals Pessimism Prosopis Trees

Most recents protocols related to «Pessimism»

No sample size calculation was done due to the exploratory nature. For the primary outcome, we performed an estimate of the precision. Given a precision of ± 3% and assumed proportion of 30% self-sampling (enrolled subjects), a sample size of 1200 WLWH estimates a 95% confidence interval completely within the required precision. In the pessimistic case with a given a precision of ± 2% and assumed proportion of only 10% self-sampling (enrolled subjects), a sample size of 1200 WLWH estimates a 95% confidence interval completely within the required precision.
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Publication 2023
Pessimism
During the pandemic, a self-made scale was used to measure residents' uncertainty. The scale contains six questions (Table 2) that asks subjects about their perceptions of the spread of the pandemic and their own infection, as well as their perceptions of the future development of the pandemic. The Likert scale with 5 points was adopted for all questions (from 1 strongly disagreed to 5 strongly agreed). The higher the subjects' average score, the higher their perception of the risk of pandemic spread and infection, and the higher their pessimistic perception of the future development of the pandemic. In this study, the Cronbach's α score for this scale was 0.93.
The scores of uncertainty ranged from 1 to 5, and 3 points indicated that the uncertainty of the subjects was in the middle level. According to the distribution of subjects' uncertainty scores (Figure 1), we divided them into two groups: the medium-high group (with scores ≥3) and the low group (with scores <3). Finally, we obtained that there were 209 subjects in the medium-high group and 311 subjects in the low group, and there were significant differences in the scores of uncertainty between such two groups [t(528) = 29.93, p < 0.001].
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Publication 2023
Infection Pandemics Pessimism
People's inherent psychological traits can have an impact on positive life attitudes, depression levels, and pandemic preventive behavior intentions. Personality, as the sum of an individual emotions, thoughts, and behavioral tendencies, plays an important role in this process. Previous studies have shown that people's mental health (33 (link)), positive life attitudes (34 (link)) and behavioral tendencies (35 (link)) are significantly influenced by personality. For example, individuals with higher neuroticism scores were more likely to be depressed and have a pessimistic outlook on life (36 (link), 37 (link)). Individuals with higher conscientiousness scores were more likely to adopt preventive behaviors during the pandemic (38 (link), 39 (link)). To exclude the interference of personality on the findings, we included personality as control variables in this study. The Big Five personality scale developed by (40 (link)) was used in this study to measure the personality of the subjects. The scale contains five dimensions: neuroticism, conscientiousness, agreeableness, extraversion, and openness, and each dimension contains eight questions. The Likert scale with 5 points was adopted for all questions (from 1 very disagreed to 5 very agreed). In this study, the Cronbach's α scores were 0.91, 0.85, 0.71, 0.94, and 0.79, respectively.
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Publication 2023
Emotions Extraversion, Psychological Mental Health Neuroticism Pandemics Pessimism Thinking
Depression was assessed using the Beck Depression Inventory short version (BDI-II) (29 ). The short form of the Beck Depression Inventory (BDI-13) is useful for screening and assessing depression in clinical and research conditions. The BDI-13 assesses the symptoms including depressed mood, pessimism, sense of failure, lack of satisfaction, self-guilt, self-hate, self-harm, social withdrawal, distorted body image, indecisiveness, work difficulty, fatigue, and loss of appetite. Abdel-Khalek translated BDI-II and studied the coefficient of alpha among samples of male and female undergraduates recruited from Egypt, Saudi Arabia, Kuwait, and Lebanon (n = 100, 80, 100, 100, respectively). Values of Cronbach's alpha were 0.77, 0.82, 0.89, and 0.67, respectively (30 (link)). The total score varies from 0 to 39. We considered the interpretation of the scores as follows: 0–3: no depression, 4–7: mild depression or light depression, 8–15: moderate depression, and 16 and above: severe depression. We recorded the BDI-II score into a dichotomous variable: without depression for the categories no and mild depression and the presence of depression for the categories moderate and severe depression.
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Publication 2023
Anorexia Body Image Fatigue Guilt Light Males Mood Pessimism Satisfaction Woman
Temperament was measured using Cloninger’s TCI, which includes four genetically independent temperament traits and their subscales: novelty seeking (exploratory excitability, impulsiveness, extravagance, and disorderliness), harm avoidance (worry/pessimism, fear of uncertainty, shyness, and fatigability), reward dependence (sentimentality, attachment, and dependence), and persistence (Cloninger, 1994). The character items of the TCI were not used in the surveys. The TCI (version IX) consist of 240 true/false items, including 107 temperament items (35 harm avoidance, 40 novelty seeking, 24 reward dependence and 8 persistence items). Temperament trait summary scores were calculated for the four main temperament traits.
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Publication 2023
Character Fear Harm Reduction Impulsive Behavior Pessimism Temperament

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More about "Pessimism"

Pessimism is a mental state characterized by a tendency to focus on negative or unfavorable aspects of a situation.
This disposition involves a general expectation that things will turn out poorly, or a lack of hope and confidence in the future.
Pessimistic individuals often exhibit a persistent focus on difficulties, a belief that problems are insurmountable, or a tendency to anticipate and prepare for the worst.
This mental state can have significant impacts on an individual's mood, decision-making, and overall well-being.
Researchers studying pessimism utilize a variety of methods, including experimental psychology, neuroscience, and data analysis (e.g., using SAS 9.4, R version 3.6.1, Stata 15, SPSS v20) to better understand the causes, consequences, and potential interventions for pessimistic thinking.
The PubCompare.ai platform offers powerful AI-driven tools to assist in this important area of research, helping scientists easily locate the most effective protocols and solutions to support their pessimism studies.
With features like protocol optimization and intelligent comparison tools, PubCompare.ai empowers researchers to identify the best practices and products to advance their work on negative outlok, gloomy disposition, and lack of hope (HO 244966, R version 4.0.2, Stata 14).
By leveraging the power of AI, researchers can enhance their pessimism studies and gain new insights into this critical aspect of mental health and well-being.