Exploring the Experiences and Impacts of Research Role-Emerging Placements in Physiotherapy
Bibliographic record
Abstract
Purpose: Research role-emerging placements (RREPs) have been integrated into placement offerings in Canadian physiotherapy programmes. The purpose of the present study is to describe the experiences and impacts of RREPs completed by graduates of Canadian physiotherapy programmes. Methods: Participants were recruited by purposive sampling and completed semi-structured interviews to explore their RREP experiences. Themes were identified using thematic analysis and collaboratively analyzed using the DEPICT model. Results: Eleven participants who completed RREPs during their Canadian physiotherapy programmes (three men, eight women; aged 26.9 [SD 2.7] years) took part in this study. The participants expressed the RREP was a valuable experience. Four themes emerged from the data: (1) Motivators for selecting an RREP included interest in research or a medical injury, (2) The RREP experience involved benefits and challenges, (3) Impacts of completing an RREP, and (4) RREP participant suggestions. Conclusions: RREPs are valuable placement opportunities for learners in Canadian physiotherapy programmes facilitating the development of essential competencies in a non-traditional setting. RREPs could be considered as a placement opportunity for other allied health programmes, as the skills gained are beneficial for all health care professionals.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".