Disclosing recovery in academia: the role of stigma and recovery capital
Bibliographic record
Abstract
Background Although research on recovery disclosure is expanding, few studies have explored how post-secondary employees experience and navigate disclosure within academic contexts that often promote substance use and threaten recovery.Methods Drawing on a Recovery Capital (RC) framework and stigma theory, we conducted a community-based participatory study to examine disclosure experiences among 10 Canadian university employees (faculty and staff) in recovery from substance use and/or behavioral addictions. Most participants were women and nonacademic staff, with an average recovery length of 7.8 years. Alcohol and binge eating emerged as the most common recovery experiences. We analyzed semi-structured interviews using reflexive thematic analysis.Results Participants described a spectrum of disclosure experiences, ranging from full openness to complete concealment, shaped by two main themes that captured the key influencing factors: 1. Social RC - Relationships as gateways and barriers to disclosure: Trust and emotional safety supported disclosure. Stigma led participants to minimize or hide their recovery, especially in relationships that felt invalidating; 2. Community RC - Navigating norms, stigma, and advocacy: Campus norms around alcohol and food shaped disclosure decisions. Some avoided campus supports due to stigma, limited availability, or lack of awareness, while others disclosed strategically to offer support and advocate for change.Conclusion Recovery disclosure among post-secondary employees is deeply relational, norms driven, and context dependent. Strengthening peer supports, visibility, and inclusive policies can reduce stigma and build more recovery-friendly academic environments.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".