Postsecondary instructors’ disclosures of mental illness and neurodiversity in the classroom: motives and means suggest four disclosure types
Bibliographic record
Abstract
Existing literature has described a diverse array of motivations for postsecondary instructors’ disclosures of mental illness in higher education classrooms. These motivations lack integration and have been discussed mainly in isolation from corresponding practices, audiences, mediums, contexts, and diagnoses. We conducted a series of focus group interviews and gathered reflective narratives and questionnaire data with six postsecondary instructors in Canada who regularly disclosed mental illness and/or neurodiversity to students. We identified four categories of motivations that reflected concerns with individual well-being, effective teaching, student success, and destigmatizing mental illness and neurodiversity. We also noted that certain motives tended to correspond with particular disclosure mediums, audiences, and schedules, yielding four disclosure types: prophylactic, andragogical, supportive, and political. These typologies bring nuance to our understanding of instructors’ disclosures of mental illness and/or neurodiversity in postsecondary classrooms and suggest ways in which institutions can support these instructors.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".