A Qualitative Exploration of Addiction Disclosure and Stigma among Faculty Members in a Canadian University Context
Bibliographic record
Abstract
Addiction is one of the most stigmatized public health issues, which serves to silence individuals who need help. Despite emerging global interest in workplace mental health and addiction, scholarship examining addiction among university faculty members (FMs) is lacking, particularly in a Canadian context. Using a Communication Privacy Management (CPM) framework and semi-structured interviews with key informants (deans and campus mental health professionals), this qualitative study aimed to answer the following research questions: (1) What is the experience of key informants who encounter FM addiction? (2) How may addiction stigma affect FM disclosure and help-seeking? and (3) What may help reduce addiction stigma for FMs? Thematic analysis was used to identify three main themes: (1) Disclosure was rare, and most often involved alcohol; (2) Addiction stigma and non-disclosure were reported to be affected by university alcohol and productivity cultures, faculty type, and gender; (3) Reducing addiction stigma may involve peer support, vulnerable leadership (e.g., openly sharing addiction-recovery stories), and non-discriminatory protective policies. This study offers novel insights into how addiction stigma may operate for FMs in relation to university-specific norms (e.g., drinking and productivity culture), and outlines some recommendations for creating more recovery-friendly campuses.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".