Bibliographic record
Abstract
Processes of professional socialization influence types of substances used, patterns of use, and estimation of normalization. This project explores psychoactive substance use among professionals and students in professional programs in Canada, rationales for use, strategies to manage use and potential consequences, and factors within professional education and culture that influence decisions about use. The intent of this study is to uncover social processes through which professional contexts influence substance use. The researchers sought to explore how professionals and professional students described their own decision-making about substance use and their perceptions of professional influences. The mixed methods pilot study involved ecological momentary assessment (EMA), using an app designed for the study, and qualitative interviews. Participants completed a brief survey on the app each time they used a substance during a 4-week period, reporting what substance was used, how much, where, who with, and anticipated or delayed effects. Thirty-four participants were involved in the EMA component, 20 of whom engaged in interviews. The findings suggest a certain amount of substance use is expected, accepted, and even promoted in professional fields. Thematic analysis revealed novel understandings about (i) deliberate decisions, (ii) disclosure and use, (iii) stigmatized substance use, (iv) normative substance use, and (v) the professional context. This study demonstrates potential advantages of undertaking research to explore substance use, as distinct from substance abuse, problematic use, dependence, or addiction. Conceptualizing substance use more broadly can help to identify factors that both encourage use (e.g., performance demands, social norms) and constrain use (e.g., responsibility, role modeling). This can expand approaches to address substance use that look beyond the individual to social and institutional contexts, acknowledging that responsibility is a collective process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".