Evaluation of a Coaching Experiential Learning Project on OT Student Abilities and Perceptions
Bibliographic record
Abstract
Innovative teaching methods to address emerging practice needs are critical components of effective occupational therapy education. Experiential learning strategies can enhance skill development and translation of knowledge into OT clinical practice. In addition, skills such as coaching may provide important links to health promotion practices. Thirty-two occupational therapy students took part in an experiential project to connect occupational engagement and health for a community of older adults. A pretest/posttest design was used to evaluate program outcomes in student perceived abilities, and narrative reflection papers provided postexperience qualitative information. The students improved in all 10 areas of abilities self-assessment with mean total scores from pretest (M = 42) improving significantly at posttest (M = 58). Themes from reflection papers indicated a positive response to experiential learning and a desire for more opportunities to prepare for clinical practice, including the use of interprofessional training. The students improved in their abilities to use coaching and health promotion strategies through the use of experiential learning methods. Outcomes suggest that experiential learning opportunities are an effective way to enhance student competencies in coaching, improve readiness for wellness programming, and increase student confidence in application of skills in future clinical practice.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".