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Substance Use

Q: What are the different types of substance use?

Substance use encompasses a wide range of behaviors, from casual use to addiction and dependency. This includes the use of legal substances like alcohol and tobacco, as well as illicit drugs such as cocaine, heroin, and methamphetamines. Each type of substance can have unique effects and risks associated with its use.

Q: What are the common challenges people face with substance use?

Substance use can lead to a variety of challenges, including physical and mental health issues, social and occupational problems, and financial difficulties. Addiction and dependency can be particularly difficult to overcome, requiring comprehensive treatment and support strategies. Managing cravings, preventing relapse, and rebuilding one's life are common hurdles faced by those struggling with substance use disorders.

Q: How can PubCompare.ai assist with substance use research?

PubCompare.ai allows researchers to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform can help identify the most effective protocols related to substance use for your specific research goals. Through AI-driven analysis, PubCompare.ai can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accyracy, ultimately streamlining your research process and supporting data-driven decisions.

Q: What are the benefits of using PubCompare.ai for substance use research?

By using PubCompare.ai, researchers can experience several benefits in their substance use research. The platform's AI-driven comparisons allow you to locate the best protocols and products from literature, pre-prints, and patents, saving time and effort. The powerful analysis features can help you identify the most effective approaches, enabling you to make data-driven decisions and enhance the quality of your research. PubCompare.ai streamlines the research process, allowing you to focus on generating meaningful insights that can improve outcomes for individuals and communities.

Substance Use refers to the consumption or misuse of legal or illegal substances, including alcohol, tobacco, and illicit drugs.
This broad term encompasses a range of behaviors and patterns, from casual use to addiction and dependency.
Substance use can have significant impacts on physical and mental health, social functioning, and overall well-being.
Effective prevention, treatment, and recovery strategies are crucial in addressing this complex public health issue.
Reserach in this field aims to better understand the causes, consequences, and management of substance use disorders to improve outcomes for individuals and communities.

Most cited protocols related to «Substance Use»

Longitudinal Study of Pre-Substance Use Development

Cited 175 times

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Publication 2018

A-factor (Streptomyces) Child Ethanol factor A Precocious Puberty Psychometrics Puberty Sound Substance Use Youth

Disaggregating Between-Person and Within-Person Effects

Cited 174 times

It is well known that between- and within-person effects can be efficiently and unambiguously disaggregated within the multilevel model using the strategy of person-mean centering. Traditionally, the term centering is used to describe the rescaling of a random variable by deviating the observed values around the variable mean (e.g.,Aiken&West 1991 , pp. 28–48). For example, within the standard fixed-effects regression model, a predictor x_i is centered via

x_{i}^{'} = x_{i} - \bar{x}

, where x̄ is the observed mean of x_i, and

x_{i}^{'}

is the mean-deviated rescaling of x_i (see, e.g., Cohen et al. 2003 , p. 261). By definition, the mean of a centered variable is equal to zero, and this offers both interpretational and sometimes computational advantages in a number of modeling applications.
However, centering becomes more complex when considering TVCs. This is because multiple repeated measures are nested within each individual, and there are thus two means to consider: the grand mean of the TVC pooling over all time points and all individuals, and each person-specific mean pooling over all time points within individual. There are two ways that we can center the TVC.
First, we can deviate the TVC around the grand mean pooling over all individuals. Here,

{\ddot{z}}_{t i} = z_{t i} - \bar{z} ‥,

where z̈_ti represents the grand mean centered TVC, z_ti is the observed TVC, and z̄‥ is the grand mean of z_ti pooling over all individuals and all time points. In other words, we simply compute the grand mean of the TVC and subtract this from each individual- and time-specific TVC score. Second, we can deviate the TVC around the person-specific mean of the TVC unique to each individual. Here,

{\dot{z}}_{t i} = z_{t i} - {\bar{z}}_{i},

where ż_ti represents the person-mean centered TVC, z_ti is again the observed TVC, and z̄_i is the person-specific mean for individual i. In other words, we subtract just the person-specific mean of the TVC from each of that same person’s time-specific TVC scores. We can use z_ti, ż_ti, or z̈_ti as the level-1 predictor in Equation 8, and each is associated with a potentially different inference with respect to the disaggregation of effects.
Methods exist that allow for the disaggregation of the between-person and within-person effects using z_ti, ż_ti, or z̈_ti (Kreft et al. 1995 , Raudenbush & Bryk 2002 ). However, direct estimates of these effects can be most easily obtained within the multilevel model by incorporating the person-mean centered TVC at level-1 (i.e., ż_ti) and the person-mean at level-2 (i.e., z̄_i) (Raudenbush & Bryk 2002 , equation 5.41). Specifically,

\begin{array}{l} y_{t i} = β_{0 i} + β_{1 i} {\dot{z}}_{t i} + r_{t i} \\ β_{0 i} = γ_{00} + γ_{01} {\bar{z}}_{i} + u_{0 i}, \\ β_{1 i} = γ_{10} \end{array}

where all is defined as above. This requires three steps: We first compute the mean of the time-specific TVCs within each individual to obtain z̄_i; we then subtract that person-specific mean from each individual’s time-specific TVC values to obtain ż_ti; finally, we use both z̄_i and ż_ti as predictors in our multilevel model.
The reduced form equation for this model is

y_{t i} = (γ_{00} + γ_{01} {\bar{z}}_{i} + γ_{10} {\dot{z}}_{t i}) + (u_{0 i} + r_{t i}),

where γ₀₀ is the intercept (or grand mean), γ₀₁ is a direct estimate of the between-person effect, and γ₁₀ is a direct estimate of the within-person effect. Following our earlier hypothetical example, γ₀₁ would capture the relation between average levels of anxiety and average levels of substance use pooling over individuals. In contrast, γ₁₀ would capture the mean relation between a given person’s time-specific deviation in anxiety (relative to the overall level of anxiety) and the individual’s time-specific substance use.
The approach we outline above is currently regarded as best practice for the disaggregation of between-person and within-person effects in multilevel growth models (e.g., Raudenbush & Bryk 2002 , pp. 181-85; Singer & Willett 2003 , pp. 173-77), and there is no question that this is a valid method for accomplishing these goals. As we describe in greater detail below, however, the validity of this approach heavily relies on a set of specific conditions that may or may not be met in practice. Further, we have found that these conditions are rarely, if ever, discussed in either the quantitative or applied literatures. To better define these specific conditions, we next propose a more general framework for defining within-person and between-person effects. This framework both more formally establishes these expressions and allows us to explicate precisely under what conditions standard approaches are and are not valid.

Curran P.J, & Bauer D.J. (2011). The Disaggregation of Within-Person and Between-Person Effects in Longitudinal Models of Change. Annual review of psychology, 62, 583-619.

Publication 2010

Anxiety Singer Substance Use

Multidimensional Assessment of Impulsivity and Risky Behaviors

Cited 135 times

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Publication 2014

Alcoholics Alcohol Use Disorder Diagnosis Eating Disorders Ethnicity Feeding Behaviors Pharmaceutical Preparations Quercus Substance Use

Reliability and Validity of DSM-5 Alcohol Use Disorder Assessments

Cited 88 times

Lifetime DSM-5 AUD diagnoses required at least 2 of the 11 criteria in the 12 months preceding the interview or previously. Diagnoses before the past 12 months required clustering of at least 2 criteria within a 1-year period. Consistent with DSM-5 criteria, AUD severity levels were classified as mild, moderate, or severe (2-3, 4-5, or ≥6 criteria, respectively). Lifetime alcohol abuse diagnoses based on DSM-IV criteria required at least 1 of the 4 abuse criteria in the 12 months preceding the interview or previously. Lifetime alcohol dependence based on DSM-IV criteria required at least 3 dependence criteria in the past 12 months or previously. Diagnoses before the past 12 months also required clustering of criteria within a 1-year period. Concordances between DSM-IV and DSM-5 12-month and lifetime AUDs in the NESARC-III were excellent (κ = 0.76 and κ = 0.61, respectively).^{23 (link)}Symptom items (n = 37) that assessed DSM-IV AUD diagnoses in the NESARC and NESARC-III were virtually identical. However, 3 items were slightly reworded and 3 additional abuse questions appeared in the NESARC-III. Comparisons between DSM-IV 12-month AUD diagnoses with and without the additional questions yielded virtually identical prevalences (12.7% and 12.2%, respectively), with near-perfect concordance (κ = 0.98), which suggested that trivial differences between AUD operationalizations were not responsible for the changes reported herein.
Test-retest reliability of AUDADIS-5 and DSM-5 AUD categorical diagnoses (κ = 0.60 and κ = 0.62, respectively) and dimensional criteria scales (intraclass correlation coefficient [ICC], 0.83 and 0.85, respectively) was substantial in a large general population sample.^{24 (link)} Procedural validity of AUDADIS-5 and DSM-5 AUD was assessed through blind clinical reappraisal using the clinician-administered, semi-structured Psychiatric Research Interview for Substance Use and Mental Disorders, DSM-5 (PRISM-5) version.²⁵ The clinical reappraisal, conducted in a large general population sample,^{26 (link)} showed fair to good concordance on AUDADIS-5 and PRISM-5 AUD diagnoses (κ = 0.49 and κ = 0.62, respectively) and excellent concordance (ICC, 0.81 and 0.85, respectively) for their dimensional counterparts.
Test-retest reliability of AUDADIS-IV and DSM-IV AUD diagnoses was good to excellent in clinical and general population samples.^{27 (link)-31 (link)} Convergent, discriminant, and construct validity of AUDADIS-IV AUD diagnoses were good to excellent,^{32 (link)-36 (link)} including in the World Health Organization/National Institutes of Health Study on Reliability and Validity (κ range, 0.60-0.70).^{37 (link)-39 (link)}

Grant B.F., Goldstein R.B., Saha T.D., Chou S.P., Jung J., Zhang H., Pickering R.P., Ruan W.J., Smith S.M., Huang B, & Hasin D.S. (2015). Epidemiology of DSM-5 Alcohol Use Disorder: Results From the National Epidemiologic Survey on Alcohol and Related Conditions III. JAMA psychiatry, 72(8), 757-766.

Publication 2015

Abuse, Alcohol Alcoholic Intoxication, Chronic Diagnosis Drug Abuse Mental Disorders prisma Substance Use Visually Impaired Persons

Distress and Depression in Diabetic Patients

Cited 57 times

Baseline samples from two studies of depression and distress among adult type 2 diabetic patients were included. Study 1, our primary sample, used baseline data from the Distress and Depression in Diabetes Study (3D Study), a noninterventional, three-wave, 18-month study of the prevalence and persistence of DD and depressive symptoms among 506 adult type 2 diabetic patients (2 (link)). Data were collected between 2003 and 2006. Study 2 used baseline, preintervention data from the Reducing Distress and Enhancing Effective Management (REDEEM) Study, a randomized controlled trial designed to reduce DD and enhance management among 392 type 2 diabetic adults (12 ). These data were collected between 2008 and 2010. Patients in both studies were recruited using the diabetes registries of several Bay Area community-based medical groups and diabetes education centers.
Inclusion criteria for both studies were patients with type 2 diabetes for 12 months or more, age 21 years or older, read and speak English fluently, no severe diabetes complications, and no active psychosis, substance use, or dementia. Additional, more restrictive criteria for REDEEM Study patients included displaying a mean item score of 1.5 or higher on the DDS2 to indicate elevated DD, displaying a score of 15 or higher on the Patient Health Questionnaire 8 to exclude patients with clinical depression, and displaying a deficit in at least one of three areas of diabetes self-management (diet, physical activity, medication use). A modification of the Summary of Diabetes Self-care Activities (SDSCA) (13 (link)) was used to define a deficit as not following their diet or physical activity plan 3 or more days during the last week or not taking prescribed diabetes medications 2 or more days during the last week.

Fisher L., Hessler D.M., Polonsky W.H, & Mullan J. (2012). When Is Diabetes Distress Clinically Meaningful?: Establishing cut points for the Diabetes Distress Scale. Diabetes Care, 35(2), 259-264.

Publication 2012

Adult Complications of Diabetes Mellitus Dementia Depressive Symptoms Diabetes Mellitus Diabetes Mellitus, Non-Insulin-Dependent Diet Patients Pharmaceutical Preparations Psychotic Disorders Self-Management Substance Use

Most recents protocols related to «Substance Use»

Characteristics of Patients with Comorbid Mental Health and Substance Use Disorders

Table 1 shows the demographic and clinical characteristics of the total sample, and for patients with COD compared to those without. Patients with COD were younger than those without COD, and more patients in the COD group had a lower education level. In addition, those with COD were more likely to have an unstable housing arrangement, and to have higher baseline and follow-up levels of mental distress. We found a significant reduction in mental distress at follow-up in both groups of patients. Moreover, the baseline motivation to change substance use behavior was as high in patients with COD as among patients without COD. With respect to types of SUD diagnoses, the results revealed that patients with COD were less likely than patients without COD to have an alcohol use disorder (F10). Patients with COD were comparatively more likely to have each of the illicit drug use disorders (i.e. cannabis use disorder, sedatives use disorder, opiate use disorder and stimulant use disorder), and polysubstance use. The data also showed that patients with COD had significantly longer treatment stays.

Table 1

Sample characteristics of patients with COD compared to patients without COD.

Variables	Total (N = 611)		COD(n = 289)		Without COD (n = 322)		COD versuswithout COD
Variables	N^a	% or mean (SD)	n	% or mean (SD)	n	% or mean (SD)	p-value	Effect size
Age (years) at intake	610	30.0 (13.9)	288	32.9 (11.6)	322	42.7 (14.1)	< 0.000	0.759
Female	175	28.7	91	31.6	84	26.2	0.139	0.060
Education level low	189	32.6	109	39.5	80	26.3	< 0.001	0.140
Unstable housing arrangement¹	210	35.1	117	41.2	93	29.6	0.003	0.121
Motivation	610	4.25 (0.77)	289	4.26 (0.78)	321	4.24 (0.75)	0.663	0.027
Mental distress baseline	611	2.15 (0.71)	289	2.30 (0.69)	322	2.02 (0.70)	< 0.001	0.403
Mental distress follow-up²	425	1.93 (0.74)	206	2.07 (0.73)	219	1.79 (0.72)	< 0.001	0.386
Improved mental distress³	425	0.22 (0.79)	206	0.22 (0.79)	219	0.23 (0.80)	0.852	0.013
Length stay (days)	611	93.8 (79.6)	289	112.2 (92.2)	322	77.2 (61.9)	< 0.001	0.446
SUD diagnoses⁴	585	95.7
- Alcohol use F10	345	59.0	132	48.4	213	68.3	< 0.001	0.202
- Opioid use F11	111	19.0	64	23.4	47	15.1	0.010	0.107
- Cannabis use F12	216	36.9	128	46.9	88	28.2	< 0.001	0.193
- Sedative use F13	170	29.1	99	36.3	71	22.8	< 0.001	0.148
- Stimulant use F15	188	32.1	105	38.5	83	26.6	0.002	0.127
- Alcohol use only	229	39.1	62	22.7	167	53.5	< 0.001	0.315
- Polysubstance use⁵	134	21.9	77	28.2	57	18.3	0.004	0.118

¹Reference: Owned or rented residence. ² For both patients with and without COD, the mean score of mental distress was lower at follow up (t = 41.15, df = 205, p < 0.001, and t = 36.66, df = 218, p < 0.001, respectively). ³ Calculated by subtracting the mean follow-up score from the mean baseline score. ⁴ More than one diagnosis could be registered for each patient. The most common two-substance combination was cannabis use and stimulant use (n = 113).⁵ Three or more SUD diagnoses

Notes: Percentages in valid percent. Categorical variables presented as valid percentages (%); continuous variables presented as mean (SD). Effect size was measured using Cohen’s d and Cramer’s V, as appropriate

Andersson H.W., Mosti M.P, & Nordfjaern T. (2023). Inpatients in substance use treatment with co-occurring psychiatric disorders: a prospective cohort study of characteristics and relapse predictors. BMC Psychiatry, 23, 152.

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Publication 2023

Alcohol Use Disorder Cannabis Diagnosis Drug Use Disorders Motivation Opiate Alkaloids Opioids Patients Respiratory Diaphragm Sedatives Substance Use Youth

Intrinsic Motivation Assessment for Substance Use

Five items concerning intrinsic motivation for changing personal substance use were used to measure baseline motivation. The items were obtained from the Circumstances, Motivation Readiness and Suitability instrument (CMRS) [31 (link)]. The patient responses were rated on a scale ranging from 1 (completely disagree) to 5 (totally agree), (α = 0.83).

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Publication 2023

Motivation Patients Substance Use

Substance Use Disorder Treatment Cohort

We used data from a prospective cohort study of patients admitted to an inpatient SUD treatment stay at five treatment centers in central Norway. Patients were recruited in the period from September 2014 to May 2016. Study participation involved providing demographic, substance use and health information, from a questionnaire and the electronic medical record, as well as a follow-up interview 3 months after the end of the inpatient stay. The Regional Ethical Committee for Medical Research in Norway reviewed the study protocol and approved the study (#2013/1733). In accordance with the Declaration of Helsinki, those who agreed to participate gave their signed consent. Dedicated research staff affiliated with the study were responsible for continuously recruiting patients admitted for inpatient treatment at the five clinics. The only exclusion criteria were persons judged mentally incapable of giving their signed consent for participation. Of 728 eligible patients, 611 (84%) consented to participate (see 15, 24 for more details).

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Publication 2023

Hospitalization Inpatient Patients Substance Use

Reduced Supervised Consumption During COVID-19

Data for this study were drawn from two open prospective cohort studies of PWUD in Vancouver, Canada: the Vancouver Injection Drug Users Study (VIDUS) and the AIDS Care Cohort to evaluate Exposure to Survival Services (ACCESS). Both cohorts have been described in detail in previous literature [24 (link), 27 (link)]. However, to briefly summarize, these cohorts have been recruiting participants through community-based methods, including street outreach, self-referral, and word of mouth since May of 1996. VIDUS includes adults (18 years and older) who are HIV-negative and have injected unregulated drugs within the month prior to their enrolment. ACCESS participants are HIV-positive adults who used any unregulated substance (other than or in addition to cannabis) within the month prior to their enrolment. Participants in the VIDUS cohort who HIV seroconvert after their enrolment are transferred to the ACCESS cohort. All participants provided written informed consent at enrolment and ethics has been approved by Providence Health Care/University of British Columbia’s Research Ethics Board. Both cohorts use harmonized study protocols to facilitate pooled analyses.
At baseline and at 6-month intervals afterwards, participants complete interviewer- and nurse-led questionnaires and provide blood samples for serology, as well as urine for drug screening. The questionnaire covers a variety of topics including demographics, substance use, healthcare access, and socio-structural exposures. To compensate participants for their involvement, participants receive a $40 CAD stipend for every study visit.
Due to the COVID-19 pandemic, all in-person data collection was suspended between March 2020 and July 2020. After July 2020, infection control measures were put in place to resume data collection. Participant interviews were completed over telephone or videoconferencing. Study-owned cell phones and private spaces were loaned to those who required them. They were then able to pick up their cash honoraria in person or have it e-transferred if they had access to a bank account.
Between March and July of 2020, study questionnaires were modified to include questions regarding the COVID-19 pandemic. One of these questions was used to assess the primary outcome of this study, which read as follows: “Has the frequency of your use of these sites [i.e., SCS/OPS] changed since the beginning of the public health emergency?”. The outcome was dichotomized using the following responses: “I use them less” vs. “I use them more” or “My use stayed the same”. Potential correlates were identified based on past studies that assessed SCS access among PWUD [8 (link), 25 (link)], and included: age (per year older), self-identified gender (man vs. woman/other), ethnicity/ancestry (white vs. Black, Indigenous, and people of colour), education (high school or greater vs. other), employment (yes vs. no), residence in Downtown Eastside neighbourhood in Vancouver (yes vs. no), daily non-medical prescription opioid use (yes vs. no), daily cocaine use (yes vs. no), daily crystal methamphetamine use (yes vs. no), daily non-injection crack-cocaine use (yes vs. no), benzodiazepine use (yes vs. no), suspected that a drug used contained fentanyl (yes vs. no), used drugs alone (yes vs. no), engagement in opioid agonist therapy (yes vs. no), non-fatal overdose (yes vs. no), witnessed an overdose (yes vs. no), experience physical violence (yes vs. no), syringe/ drug use equipment sharing (yes vs. no), inability to access treatment (yes vs. no), unstable housing (yes vs. no), sex work (yes vs. no), incarceration (yes vs. no), jacked up (this refers to being stopped, searched, or detained) by the police (yes vs. no), cohort/ HIV status (ACCESS vs. VIDUS), ever tested positive for COVID-19 (yes vs. no), concern about COVID-19 on a scale from 1 to 10, with 10 indicating greatest concern (1–5 vs. 6–10), any chronic health conditions (yes vs. no), and ease of accessing SCS/OPS changed since COVID-19 (same vs. easier vs. harder). All drug use and behavioral variables refer to the 6 months prior to questionnaire date unless otherwise indicated.
Univariable and multivariable logistic regression analyses were used to assess the associations between the correlates of interest and reduced frequency of SCS/OPS use since COVID-19. Correlates of interest with a univariable p-value < 0.10 were included in a backward elimination procedure, with the least significant variable removed at each step until the lowest Akaike Information Criterion (AIC) was achieved. All p-values were two-sided and all statistical analyses were conducted using SAS version 9.4 (SAS Institute, Cary, North Carolina, United States).

Gubskaya E., Kennedy M.C., Hayashi K., Cui Z., Milloy M.J, & Kerr T. (2023). The impact of the COVID-19 pandemic on access to supervised consumption programs. Substance Abuse Treatment, Prevention, and Policy, 18, 16.

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Publication 2023

Abuse, Physical Acquired Immunodeficiency Syndrome Adult Benzodiazepines BLOOD Cannabis Chronic Condition Cocaine COVID 19 Crack Cocaine Drug Abuser Drug Overdose Emergencies Ethnicity Fentanyl Gender Infection Control Interviewers Methamphetamine Nurses Opioids Oral Cavity Pharmaceutical Preparations Substance Use Urine Woman

Willingness to Use Digital HIV Prevention Apps Among Black Women

Cited 1 time

Survey data contained items measuring sociodemographic sources of health information, and items from the Willingness to Use Mobile Phone Apps for HIV prevention Survey by Goedel et al [17 (link)]. This study was initially developed to measure the acceptability of mobile phone apps for HIV prevention among MSM [17 (link)]. At the time of this study, there were very few studies on HIV and digital health among Black women compared with that among MSM and transgender populations. Therefore, the research team adapted this survey and contextualized questions about Black women. We obtained authorization from the authors for access and use of the survey codebook, which was adapted for our study through an extensive literature review looking at surveys developed for Black women. Surveys were administered electronically to participants. Sociodemographic information included race, age, sex, education status, and whether the participants owned a smartphone. Frequency in accessing sources of health information was asked, in which participants ranked 10 sources of health information (1=most frequently used; 10=least frequently used). Willingness to use app features (eg, HIV at-home test kits, GPS location, condom ordering services, and pre-exposure prophylaxis [PrEP] resources), willingness to share medical test results with health care providers and current or past partners through the app, willingness to use apps for HIV prevention information, and preferences and attitudes toward PrEP were assessed using a 7-point Likert Scale (eg, 1=strongly disagree; 7=strongly agree). Recent sexual behaviors, recent substance use or abuse, HIV status, and mobile phone use were assessed using multiple-choice questions.

Tesema N., Guillaume D., Francis S., Paul S, & Chandler R. (2023). Mobile Phone Apps for HIV Prevention Among College-Aged Black Women in Atlanta: Mixed Methods Study and User-Centered Prototype. JMIR Formative Research, 7, e37987.

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Publication 2023

Condoms CTSB protein, human Drug Abuse Fingers Population Group Pre-Exposure Prophylaxis Substance Use Testing, HIV Transgendered Persons Woman

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What are the different types of substance use?

What are the common challenges people face with substance use?

Substance use can lead to a variety of challenges, including physical and mental health issues, social and occupational problems, and financial difficulties. Addiction and dependency can be particularly difficult to overcome, requiring comprehensive treatment and support strategies. Managing cravings, preventing relapse, and rebuilding one's life are common hurdles faced by those struggling with substance use disorders.

How can PubCompare.ai assist with substance use research?

PubCompare.ai allows researchers to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform can help identify the most effective protocols related to substance use for your specific research goals. Through AI-driven analysis, PubCompare.ai can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accyracy, ultimately streamlining your research process and supporting data-driven decisions.

What are the benefits of using PubCompare.ai for substance use research?

By using PubCompare.ai, researchers can experience several benefits in their substance use research. The platform's AI-driven comparisons allow you to locate the best protocols and products from literature, pre-prints, and patents, saving time and effort. The powerful analysis features can help you identify the most effective approaches, enabling you to make data-driven decisions and enhance the quality of your research. PubCompare.ai streamlines the research process, allowing you to focus on generating meaningful insights that can improve outcomes for individuals and communities.

More about "Substance Use"

Substance use encompasses the consumption or misuse of legal and illegal substances, including alcohol, tobacco, and illicit drugs.
This broad term covers a range of behaviors, from casual use to addiction and dependency.
Substance use can have significant impacts on physical and mental health, social functioning, and overall well-being.
Effective prevention, treatment, and recovery strategies are crucial in addressing this complex public health issue.
Research in this field aims to better understand the causes, consequences, and management of substance use disorders to improve outcomes for individuals and communities.
Related terms and abbreviations include substance abuse, chemical dependency, drug use, and SUD (substance use disorder).
Subtopics may include epidemiology, risk factors, screening and assessment, evidence-based interventions, harm reduction, medication-assisted treatment, and relapse prevention.
Researchers can utilize statistical software like SAS 9.4, Stata 15, SPSS version 22.0, and Stata version 16 to analyze data and inform substance use research and policies.
By optimizing research protocols with AI-driven comparisons, researchers can streamline their process and make data-driven decisions to advance the field of substance use.
With the right tools and insights, we can work towards a future where substance use is better understood and effectively managed.