Implementation and Evaluation of Online Life Skills Training Modules for Therapy Assistant Students at a Canadian College
Bibliographic record
Abstract
Like all college students, therapy assistant students may face challenges in daily living skills, such as money management, time management, and healthy meal preparation, which may negatively impact their academic and practicum success. Therapy assistant students face the added challenge of working on life skills with clients, and, as a result, students’ own life skills may affect their success in clinical encounters. Few life skills training programs exist for post-secondary students, and we were unable to find any for therapy assistant students. This study is the third phase of a larger research project that developed, implemented, and evaluated life skills training modules for therapy assistant students. Life skills training modules were offered online to therapy assistant students at a Canadian college to explore whether life skills training increased students’ knowledge, self-rated competence in occupations, and self-efficacy related to personal life skills. Findings revealed that students’ knowledge quiz scores significantly improved, and students rated the modules positively in respect to learning and satisfaction. No significant change was detected in students’ Occupational Self Assessment (OSA) scores. Online life skills training modules may be beneficial for therapy assistant students to increase their knowledge about life skills and meet identified needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".