Clinicians use courses and conversations to change practice, not journal articles: is it time for journals to peer-review courses to stay relevant?
Bibliographic record
Abstract
Academic publishing is rolling in profits1 but universities and governments are fighting back against access fees adding to threats to the business that include Plan S and Sci-Hub. Clinical scientific journals were the practitioner’s link to research findings with peer review providing quality assurance. The rise of predatory journals makes it even harder for busy clinicians to sift and appraise the ever-increasing sea of available evidence in these journals. In an effort to uncover what actually influences practice in 2020 we surveyed over 2000 sports and musculoskeletal physiotherapists on the source of the most recent change in their clinical practice. Specifically, we asked the simple question: ‘Think about the most recent aspect of your clinical practice that you changed. How did you learn about this?’ Scientific publications are not commonly used as primary sources of information to make changes to clinical practice—about 90% of respondents cited other sources (figure 1). The largest categories of responses were ‘interactions with colleagues’ and ‘attending private education short courses’ which comprised about half of all responses (the interested reader is invited to investigate these findings further …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".