Rigorous qualitative research in sports, exercise and musculoskeletal medicine journals is important and relevant
Bibliographic record
Abstract
Qualitative research enables inquiry into processes and beliefs through exploration of narratives, personal experiences and language.1 Its findings can inform and improve healthcare decisions by providing information about peoples’ perceptions, beliefs, experiences and behaviour, and augment quantitative analyses of effectiveness data. The results of qualitative research can inform stakeholders about facilitators and obstacles to exercise, motivation and adherence, the influence of experiences, beliefs, disability and capability on physical activity, exercise engagement and performance, and to test strategies that maximise physical performance. High-quality qualitative research can also enrich interpretation of quantitative analyses and be pooled in metasyntheses for evaluation of strength of evidence; contribute to the development and implementation of clinical decision support aids, outcome measures and clinical practice guidelines2 such as the UK National Institute for Health and Care Excellence guidelines (www.nice.org.uk) and Ottawa Panel guidelines for knee osteoarthritis3; and inform health and social care.4 In 2000, just 0.6% of papers in 170 general medical, mental health …
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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 | 0.064 | 0.258 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.024 | 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".