Are we preparing Victorian general practice registrars to be confident in all aspects of primary care paediatrics?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: General practitioners provide essential primary care to paediatric patients. The aim of this study was to explore associations between prevocational paediatric experiences of general practice registrars and their confidence in providing paediatric care in the general practice setting. METHOD: This was a cross-sectional observational study. Paediatric experiences and level of confidence ratings were collected using an online survey emailed to 530 Victorian general practice registrars in 2017; the response rate was 41% (217/530). Analysis used descriptive statistics, cross tabulation and Fishers' exact test. RESULTS: The most common paediatric training was undertaken in a general hospital emergency department (180/197, 91%). The majority of registrars reported that they felt confident or very confident in managing acute presentations (92% for upper respiratory tract infection, 80% for asthma, 81% for immunisation), but fewer were confident in managing mental health, behavioural or developmental presentations (all <36%). DISCUSSION: Registrars felt more confident managing acute presentations. However, the predominantly hospital-based prevocational paediatric training offers limited exposure to - and, thus, confidence in - managing behavioural, mental health and developmental issues. Training opportunities to address this identified gap should be explored.
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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 | 0.014 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".