Bibliographic record
Abstract
In a recent international survey, readers of Physiotherapy Canada noted that although they are interested in the journal's current content, they want more articles of immediate clinical relevance (see Figure 1).After reading these results, the question for the editorial team was How can Physiotherapy Canada better facilitate this?Often, busy clinicians lack the time to read the articles in the journal.When they do, they may find the studies too specific, or may feel uncomfortable changing their practice based on research results.Fortunately, there are some approaches to facilitate bringing research findings closer to implementation in practice.By synthesizing findings from several studies, systematic reviews-such as the two included in this issue, on deep vein thrombosis and exercise intervention for persons with metastatic cancer-can help physiotherapists keep up to date on the literature.While Kerse et al. 1 have shown that being aware of systematic reviews does not always translate to application in practice, other approaches, such as local champions who promote change in practice and interactions with colleagues, can enable clinicians to integrate research results in their day-to-day management of patients.Another approach to make research findings more clinically relevant is the inclusion of clinical commentaries, such as the one by Patterson in this issue, that highlight the findings of a specific study.Yet another approach is to include in articles a ''key messages'' section highlighting what the findings imply for clinical practice.In this issue of Physiotherapy Canada, you'll find all three approaches to help you translate research into practice.The challenge for any journal, and particularly for one that serves both primary research and knowledge translation functions, is to strike a balance between building the science base of the field and changing practice based on science.We continue to explore ways and means of serving and growing with the profession.
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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 | 0.202 | 0.521 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.045 | 0.037 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.043 | 0.058 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".