“You Are You, But You Are Also Your Profession”: Nebulous Boundaries of Personal Substance Use
Bibliographic record
Abstract
This paper explores Canadian professionals’ engagement in licit, illicit, and pharmaceutical substance use, their perspectives on what constitutes professional misconduct and conduct unbecoming in relation to substance use, and the dilemmas they face around self-disclosure in the context of professional regulation and social expectations. The study involved semi-structured, dialogical interviews with n = 52 professionals. Key findings are: (i) professionals do indeed use and have a history of using licit, illicit, and pharmaceutical substances, (ii) there is lack of consensus about expectations for professional conduct of substance use in one’s private life and an apparent lack of knowledge about legislation, jurisdiction of regulatory bodies, workplace policy, and workplace rights, and (iii) professionals use high discretion about personal disclosure of substance use to mitigate risk to public reputation and professional standing. Given the real potential for negative consequences associated with self-disclosure of substance use, professionals modify their use to be more consistent with perceived social standards and/or protect knowledge about their use from public disclosure. This can perpetuate assumptions that substance use by professionals is “unbecoming” and risks basing decisions and policies on incomplete and inadequate knowledge. Societally, classist ideologies that position professionals as distinct from non-professionals are reified.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.045 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".