Where do young children in specialty care come from?: a preliminary investigation of the role of primary care physicians.
Bibliographic record
Abstract
INTRODUCTION: Young children with mental health and developmental problems may not receive appropriate or timely interventions. Primary care physicians are well positioned to play an important role in the early identification and referral of such children. The objective of this pilot study was to explore the role primary care physicians played for a group of such children. METHODS: A single mailing of 1196 self-report questionnaires were sent to parents/caregivers of children under six years of age at four specialty centers in Ontario and Alberta. Key items on the survey included the role of primary care physicians (family physicians and community paediatricians). RESULTS: Twenty percent of parents/caregivers returned questionnaires. All children saw either a family physician or a community paediatrician, while 65% saw both. Families were more likely to have come to the specialty centre via a referral from a community paediatrician than a family physician. Ten percent reportedly received no referrals from a primary care physician, while 21% did not receive a referral to a specialty centre from these providers. CONCLUSIONS: The majority of children received at least one referral from a primary care physician. Further inquiry is required to determine the timeliness and appropriateness of these referrals.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| 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".