Identifying competencies required for medication prescribing for general practice residents: a nominal group technique study
Bibliographic record
Abstract
BACKGROUND: Teaching of medication prescribing is a specific challenge in general practice curriculum. The aim of this study was to identify and rank the competencies required for prescribing medication for general practice residents in France. METHODS: Qualitative consensus study using the nominal group technique. We invited different stakeholders of the general practice curriculum and medication use in primary care to a series of meetings. The nominal group technique allowed for the quick development of a list of consensual and ranked answers to the following question: "At the end of their general practice curriculum, in terms of medication prescribing, what should residents be able to do?". RESULTS: Four meetings were held that involved a total of 31 participants, enabling the creation of a final list of 29 ranked items, grouped in 4 domains. The four domains identified were 'pharmacology', 'regulatory standards', 'therapeutics', and 'communication (both with patients and healthcare professionals)'. Overall, the five items the most highly valued across the four meetings were: 'write a legible and understandable prescription', 'identify specific populations', 'prescribe the doses and durations following the indication', 'explain a lack of medication prescription to the patient', 'decline inappropriate medication request'. The 'communication skills' domain was the domain with the highest number of items (10 items), and with the most highly-valued items. CONCLUSION: The study results suggest a need for developing general practice residents' communication skills regarding medication prescribing.
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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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".