Bibliographic record
Abstract
Following on from the previous thematic edition dedicated to an examination of progress in the evidence-based health care debate, we have focussed the current issue on developments in clinical care within the primary health care setting. The first three articles, contributed by Rushmer and her colleagues from St. Andrews University, Scotland, UK, are devoted to the concept of the ‘learning practice’ in terms of its characteristics, evolution and functioning. This impressive trio of articles is prefaced by a commentary from Glyn Elwyn and Hailey who, writing from the University of Wales, bring their own particular perspective to the arguments advanced by Rushmer et al. and the Journal welcomes further debate on the key points that have been made and the contentions that have become apparent. In the articles which immediately follow we move from the UK to Canada to the contribution from Pluye and Grad who are concerned to examine how information retrieval technology may impact on physician practice, with specific reference to the organizational case study drawn from family medicine that they employ in illustration of their thinking. Returning to the University of Wales, UK, Edwards and Glyn Elwyn report the results of their study of the involvement of patients in decision making and in communicating risk through a longitudinal evaluation of doctors’ attitudes and confidence during a randomized trial. A further and integrally related article, contributed from the same group but led by Cohen and his colleagues, examines the resource effects of training general practitioners in risk communication skills and shared decision making competences. Moving from the University of Wales to the University of London, Michie and her associates present a theory-based study focussed on achieving national health targets in primary care from which we move to an important article by Wensing and his co-workers who, writing from Germany and the Netherlands, evaluate the impact of quality circles in improving prescribing patterns in primary medical care. The remaining two articles of this edition are concerned with the evaluation of a GP out-of-hours service focussed on initiatives aimed at meeting patient expectations of available standards of care, contributed by Thompson and her colleagues from the University of Ulster and the Southern Health and Social Services Board, Northern Ireland, UK, and with the use of iron deficiency tests for colorectal cancer screening within the general practice setting.
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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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.113 | 0.098 |
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".