Future Rehabilitation Professionals' Intentions to Use Self-Management Support: Helping Students to Help Patients
Bibliographic record
Abstract
Purpose: We evaluated whether education in self-management support (SMS) increases future clinicians' intentions to use a new way of delivering rehabilitation services. Methods: A convenience sample of 10 students took a 5-week theoretical course, followed by 6 weeks spent assessing patients, establishing treatment plans, and monitoring their performance by telephone. Focus groups were held before and after the educational modules, with deductive mapping of themes to the Theory of Planned Behaviour and inductive analysis of additional themes. Results: Five themes and 22 subcategories emerged from the deductive–inductive focus group content analysis. After participating in the educational modules, students reported gaining knowledge about SMS and highlighted the lack of similar preparation during their academic courses. Nonetheless, they were hesitant to adopt SMS. Conclusion: Future clinicians gained knowledge and skills after being exposed to SMS courses, but their intention to adopt SMS in their future daily practice remained low. We also noted a lack of formal training in SMS in the academic setting. The findings from this study support incorporating SMS training into the curriculum, but to increase students' intention to use SMS as part of patient care, training may need to be in more depth than it was in the modules we used.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".