General practitioner perspectives on referrals to paediatric public specialty clinics.
Bibliographic record
Abstract
BACKGROUND: Changes in the demography of Australia have resulted in changes in patterns of primary care delivery. One of these changes is that the proportion of paediatric visits has decreased. OBJECTIVE: The objectives of the article are to examine patient, practice and personal factors that influence a general practitioner's (GP's) decision to refer patients for paediatric specialty care, and investigate referral goals and experience with shared care. METHODS: A mail survey was sent out to 400 GPs who had referred at least two children to public hospital specialty clinics during 2014. RESULTS: The response rate for the mail survey was 67%. The factors most commonly reported by GPs as 'Somewhat important' or 'Very important' in the decision to refer were whether they had enough knowledge of a specific condition (81%) or did not have experience with similar patients (75%). About one-quarter (26%) of GPs reported that a parental request 'Frequently' or 'Almost always' influenced their referral decision. A similar pro-portion (26%) placed importance on whether they had sufficient time for a specific patient. DISCUSSION: Understanding the perspectives and determinants of GP referrals for paediatric specialty care is important, especially in the context of changing patterns of primary care delivery.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".