Retrospective Review of Student Research Projects in a Canadian Master of Science in Physical Therapy Programme and the Perceived Impact on Advisors' Research Capacity, Education, Clinical Practice, Knowledge Translation, and Health Policy
Bibliographic record
Abstract
Purpose: This study's aim was to characterize the nature of students' research conducted for a Master of Science in Physical Therapy (MScPT) degree programme at a Canadian university and evaluate the lead advisors' perspectives of its impact on their research capacity, education, clinical practice, knowledge translation, and health policy. Methods: We conducted a quantitative, cross-sectional, retrospective review of research reports from 2003 to 2014 to characterize the MScPT students' research and a quantitative, cross-sectional e-survey of lead research advisors to evaluate the impact of this research. Results: Data were abstracted from reports of 201 research projects completed between 2003 and 2014. Projects were conducted primarily in university-affiliated hospitals (41.6%) or the university's physical therapy department (41.1%), and the majority (52.5%) had a clinical focus. Of the 95 lead advisors of 201 projects, 59 advisors (response rate 62.1%) of 119 projects completed the survey questionnaire. The respondents most frequently identified clinical practice (45.1%) and advisors' research capacity (31.4%) as areas positively affected by the students' research. Conclusions: The MScPT students' research projects facilitate the conduct of extensive research internally and across affiliated hospitals. This research appears to advance not only clinical practice but also the ability of lead advisors to undertake research.
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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 | 0.101 | 0.291 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".