Does specialist physician supply affect pediatric asthma health outcomes?
Bibliographic record
Abstract
BACKGROUND: Pediatrician and pediatric subspecialist density varies substantially among the various Canadian provinces, as well as among various states in the US. It is unknown whether this variability impacts health outcomes. To study this knowledge gap, we evaluated pediatric asthma admission rates within the 2 Canadian provinces of Manitoba and Saskatchewan, which have similarly sized pediatric populations and substantially different physician densities. METHODS: This was a retrospective cross-sectional cohort study. Health regions defined by the provincial governments, have, in turn, been classified into "peer groups" by Statistics Canada, on the basis of common socio-economic characteristics and socio-demographic determinants of health. To study the relationship between the distribution of the pediatric workforce and health outcomes in Canadian children, asthma admission rates within comparable peer group regions in both provinces were examined by combining multiple national and provincial health databases. We generated physician density maps for general practitioners, and general pediatricians practicing in Manitoba and Saskatchewan in 2011. RESULTS: At the provincial level, Manitoba had 48.6 pediatricians/100,000 child population, compared to 23.5/100,000 in Saskatchewan. There were 3.1 pediatric asthma specialists/100,000 child population in Manitoba and 1.4/100,000 in Saskatchewan. Among peer-group A, the differences were even more striking. A significantly higher number of patients were admitted in Saskatchewan (590.3/100,000 children) compared to Manitoba (309.3/100,000, p < 0.0001). CONCLUSIONS: Saskatchewan, which has a lower pediatrician and pediatric asthma specialist supply, had a higher asthma admission rate than Manitoba. Our data suggest that there is an inverse relationship between asthma admissions and pediatrician and asthma specialist supply.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".