Publishing qualitative research in medical journals
Bibliographic record
Abstract
Qualitative research makes an important contribution to research in the medical sciences. It has a particular role in providing understanding with respect to decisions and behaviours of patients and professionals, in exploring factors affecting the implementation of new interventions, and in developing theory in fields such as illness behaviour, clinical decision making, illness prevention, and health promotion. Qualitative research articles account for almost a quarter of submissions to the BJGP, with a similar acceptance rate for publication. About a quarter of the 40 most highly cited articles published in the BJGP in recent years employ qualitative methods.
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 | 0.248 | 0.651 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.034 | 0.038 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.015 | 0.007 |
| Insufficient payload (model declined to judge) | 0.139 | 0.037 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".