Only Human: Mental-Health Difficulties Among Clinical, Counseling, and School Psychology Faculty and Trainees
Bibliographic record
Abstract
How common are mental-health difficulties among applied psychologists? This question is paradoxically neglected, perhaps because disclosure and discussion of these experiences remain taboo within the field. This study documented high rates of mental-health difficulties (both diagnosed and undiagnosed) among faculty, graduate students, and others affiliated with accredited doctoral and internship programs in clinical, counseling, and school psychology. More than 80% of respondents ( n = 1,395 of 1,692) reported a lifetime history mental-health difficulties, and nearly half (48%) reported a diagnosed mental disorder. Among those with diagnosed and undiagnosed mental-health difficulties, the most common reported concerns were depression, generalized anxiety disorder, and suicidal thoughts or behaviors. Participants who reported diagnosed mental disorders endorsed, on average, more specific mental-health difficulties and were more likely to report current difficulties than were undiagnosed participants. Graduate students were more likely to endorse both diagnosed and undiagnosed mental-health difficulties than were faculty, and they were more likely to report ongoing difficulties. Overall, rates of mental disorders within clinical, counseling, and school-psychology faculty and trainees were similar to or greater than those observed in the general population. We discuss the implications of these results and suggest specific directions for future research on this heretofore neglected topic.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".