[Legal competence problems among general practitioners].
Bibliographic record
Abstract
BACKGROUND: We wanted to investigate to what extent Norwegian general practitioners (GPs) working within a patient list system have patients who they are legally incompetent to treat, what services they offer these patients, and what attitudes a representative sample of the GPs has towards situations where one is asked to offer services to such patients. MATERIAL AND METHODS: A questionnaire was sent to 622 randomly chosen GPs. We registered sex, age, list size, size of the local community and health region for every practice. For eight hypothetical situations, we recorded whether the doctor clearly, probably or hardly would offer services. RESULTS: About one quarter of the doctors had their spouses and own children below 18 on their list. Many had secretaries or colleagues on the list. From 18% to 31% of the doctors confirmed that they have prescribed reimbursable prescription drugs to one such patient. There were great variations in views on legal competence to treat in these hypothetical situations. INTERPRETATION: GPs encounter problems of legal competence to treat. Many were of the view that a pragmatic approach is needed in day-to-day general practice. GPs should be aware of their own practice and aware of both legal competence problems and other challenges induced by having family, close friends and co-workers on the list.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".