Multi-level Analysis for Geographical Inequalities on Ambulatory Care Sensitive Hospitalizations
Bibliographic record
Abstract
Ambulatory care sensitive conditions (ACSC) hospitalizations are potentially preventable events and considered as indicator of the efficiency of the primary healthcare system. Therefore, a high level of geographic variation in ACSC hospitalizations warrants more research. The objective of current research was to assess the variation in odds of ACSC-related hospitalizations across Canadian communities and health regions. To do so, the Discharge Abstract Database (DAD) from the Canadian Institute of Health Information (CIHI), was linked to the long-form census by Statistics Canada. Data from three fiscal years (FY), (2006 to 2009), were pooled. Statistical analysis included hierarchical three-level mix modeling. Results of my study showed that between 2006 and 2009, out of 4305400 Canadian population aged below 75 years age, 29130 individuals were hospitalized because of ACSC diseases. This study indicates that up to 14.62 % of variation in the odds of ACSC-related hospitalization was attributable to general contextual factors at the Census Subdivision (CSD)-level, 1.13% was accounted by health regions and the remaining 84% was related to individual-level variations. In summary, results suggest high geographic variation in the odds of ACSC hospitalization across CSDs and health regions. Beyond urbanicity characteristics, the place of residence (CSDs) appeared as a more influential attribute for the odds of ACSC compared to the place within which primary or acute healthcare services were received (health regions).
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".