Is there room for resilience? A scoping review and critique of substance use literature and its utilization of the concept of resilience
Bibliographic record
Abstract
Research in the area of illicit substance use remains preoccupied with describing and analyzing the risks of people who use drugs (PWUD), however more recently there has been a drive to use a strengths-based or resilience approach as an alternative to investigating drug use. This leads us to ask: what can be known about PWUD from the point of view of resilience? The objective of this scoping review is to analyze how the concept of resilience is defined, operationalized, and applied in substance use research. Popular health, social science, psychology, and inter-disciplinary databases namely: SCOPUS, PUBMED, PsycINFO, and Sociological Abstracts were searched. Studies were selected if they used the concept of resilience and if substance use was a key variable under investigation. A total of 77 studies were identified which provided a definition of resilience, or attempted to operationalize (e.g., via scales) the concept of resilience in some manner. Data were charted and sorted using key terms and fundamental aspects of resilience. The majority of studies focus on youth and their resistance to, or engagement in, substance use. There is also a small but growing area of research that examines recovery from substance addiction as a form of resilience. Very few studies were found that thoroughly investigated resilience among PWUD. Consistently throughout the literature drug use is presented as a 'risk factor' jeopardizing one's ability to be resilient, or drug use is seen as a 'maladaptive coping strategy', purporting one's lack of resilience. Currently, substance use research provides a substantial amount of information about the internal strengths that can assist in resisting future drug use; however there is less information about the external resources that play a role, especially for adults. Though popular, outcome-based conceptualizations of resilience are often static, concealing the potential for developing resilience over time or as conditions change. Studies of resilience among PWUD predominantly concentrate on health-related behaviours, recovery-related factors or predefined harm reduction strategies. Indeed, overall, current conceptualizations of resilience are too narrow to recognize all the potential manifestations of resilience practices in the daily lives of individuals who actively use drugs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.123 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".