New Zealand general practice registrars’ views on their academic learning needs during vocational training: online survey
Bibliographic record
Abstract
Most doctors working in New Zealand general practice undertake vocational training through the Royal New Zealand College of General Practitioners. We aimed to explore general practice registrar views on their academic learning needs during and following vocational training. An online survey of all current NZ GP trainees in 2019 was completed by 314 registrars (54% response rate). The majority (88%, 275/314) were completing RNZCGP Fellowship only, and of these half (55%, 152/275) were planning a further postgraduate qualification. A minority (12%, 33/275) indicated a desire to undertake a masters or PhD degree. Almost all (99%, 310/314) intended to work in general practice; 9% (8/314) intending to also work as rural hospital doctors. The five most common areas of interest for further training were clinical skills (68%), practice-based teaching (66%), specific clinical conditions (63%), age or life-stage specific (47%) and non-clinical areas (41%). There is a considerable gap between completing RNZCGP Fellowship, intending to undertake further (formal postgraduate) education and actually enrolling. This is concerning given the need for lifelong learning and critical evaluation of practice and health service delivery. The future New Zealand general practice workforce needs GPs to be diverse and highly skilled members or leaders of expert teams.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".