Dissemination of Student Research in a Canadian Master of Science in Physical Therapy Programme
Bibliographic record
Abstract
PURPOSE: To determine the extent of presentation and publication, as well as time to publication, of student research completed as a component of a Master of Science in Physical Therapy (MScPT) degree at a Canadian university. METHOD: The authors conducted a retrospective cross-sectional study of MScPT research projects completed between 2003 and 2009, each undertaken by a group of MScPT students who carried out protocol development, ethics submission, data collection, analysis, and manuscript and poster preparation under the supervision of research advisors. Research advisors were e-mailed a request for citations of presentations and publications. RESULTS: Advisors from 102 of 113 research projects completed from 2003 through 2009 provided information, for a response rate of 90.3%. Of the 102 groups, 53.9% disseminated findings through publication or presentation, 33.3% presented at one or more conferences, and 30.4% published at least one peer-reviewed journal article. Median time to publication was 21 months. Almost half the journal articles (47%) were published in Physiotherapy Canada. CONCLUSIONS: MScPT student research groups are disseminating their findings through publication or presentation at a moderate rate. Investigation of factors influencing dissemination is needed to develop strategies to facilitate knowledge transfer.
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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 | 0.056 | 0.221 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".