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Record W3094306773 · doi:10.1136/bjsports-2020-102736

Clinicians use courses and conversations to change practice, not journal articles: is it time for journals to peer-review courses to stay relevant?

2020· article· en· W3094306773 on OpenAlexaff
Rod Whiteley, Christopher Napier, Nicol van Dyk, Christian J. Barton, Tim Mitchell, Darren Beales, Vasileios Korakakis

Bibliographic record

VenueBritish Journal of Sports Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsSimon Fraser UniversityUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersQatar National Research Fund
KeywordsPublishingClinical PracticeMedical educationQuality (philosophy)Peer reviewPlan (archaeology)MedicinePublic relationsPsychologyFamily medicinePolitical science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.262
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.092
GPT teacher head0.397
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations18
Published2020
Admission routes1
Has abstractyes

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