Factors Associated with Publication of Research Projects from a Canadian Master of Science Degree Programme in Physical Therapy
Bibliographic record
Abstract
Purpose: The purpose of this study was to describe the nature and extent of publications and to evaluate whether lead advisor role and experience, data collection tool, sample size, and research topic predict publication for research projects completed as part of a Canadian Master of Science in Physical Therapy (MScPT) programme. Method: We conducted a quantitative, cross-sectional, retrospective review of projects completed between 2003 and 2015 and confirmed publication status through citations of published work, a literature search, and a survey of advisors. We used descriptive statistics to describe the nature and extent of publications and logistic regression to analyze potential predictor variables. Results: Between 2003 and 2015, 44.5% of the 218 projects completed were associated with at least one peer-reviewed journal publication, and there was a seven-fold increase in annual publication rate. Projects led by a scientist or researcher ([OR] = 3.09; 95% CI: 1.15, 8.35), qualitative projects with 10 or more participants ([OR] 6.22; 95% CI: 1.96, 19.78), and quantitative projects with more than 50 participants ([OR] = 2.29; 95% CI: 1.14, 4.63) were associated with an increased likelihood of publication. Conclusions: MScPT research is published at a moderate rate, and annual publication rates increased between 2003 and 2015. Encouragement to obtain adequate sample sizes and additional support for clinician-led projects may enhance publication rates and, ultimately, bridge gaps in research-to-practice integration.
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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 | 0.081 | 0.432 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.025 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".