Student-led occupational performance coaching in a university setting
Bibliographic record
Abstract
Introduction: University students are at a high risk for mental health difficulties. Occupational performance coaching is an action-oriented and strengths-based approach that can help clients set and accomplish personally valued goals. We carried out a pilot project to evaluate a tele-rehabilitation occupational performance coaching service for university students within an occupational therapy fieldwork placement. Method: A pretest post-test design was used. Severity of symptoms of depression, anxiety and stress and goal achievement were measured before and after the intervention and semi-structured interviews were carried out with student clients and student therapists. Development of student therapist competencies were noted. Results: Thirty-five student clients enrolled in occupational performance coaching and participated in 1-6 sessions. They identified academic, health, social and vocational goals. Through occupational performance coaching, clients made important progress on their goals. Following occupational performance coaching, clients demonstrated statistically significant improvement in anxiety and depression. The occupational therapy students attained competencies comparable in number and level to those achieved in more traditional placements. Conclusion: Occupational performance coaching is a potentially valuable addition to student mental health services. Such a service can be provided by supervised fieldwork students.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".