Effect of primary health care reforms in the province of Newfoundland and Labrador: Interrupted time-series analysis.
Bibliographic record
Abstract
OBJECTIVE: To examine the effects of primary health care (PHC) reforms in the Canadian province of Newfoundland and Labrador on ambulatory care-sensitive (ACS) hospitalization rates and mortality. DESIGN: Interrupted time-series analysis of administrative data. SETTING: All communities in the province of Newfoundland and Labrador were divided into 3 groups: rural reform (n = 69 143), rural nonreform (n = 228 914), and urban nonreform (n = 197 012). No urban communities introduced PHC reforms. PARTICIPANTS: All residents of the province who held a valid health card and did not change their address during the 2001-2009 study period were included. Individuals were assigned to 1 of the 3 study groups based on community of residence. MAIN OUTCOME MEASURES: Hospitalization rates for ACS conditions, hospitalization rates for control conditions, and ACS-related mortality were compared using interrupted time-series models. RESULTS: < .01). CONCLUSION: Primary health care reforms in Newfoundland and Labrador had no observed effect on ACS hospitalization rates, but a potential effect might have been masked by a decreasing trend that preceded the introduction of reforms. The increase in mortality rates that was reversed after the introduction of reforms cannot be attributed to the reforms because it occurred in all studied populations including those that did not introduce reforms.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".