First Nations’ hospital readmission ending in death: a potential sentinel indicator of inequity?
Bibliographic record
Abstract
In this study, we focused on readmissions for Ambulatory Care Sensitive Conditions (ACSC) ending in death, to capture those admissions and readmissions that might have been prevented if responsive primary healthcare was accessible. We propose this as a sentinel indicator of equity. We conducted analyses of Manitoba-based 30-day hospital readmission rates for ACSC which resulted in death, using data from 1986-2016 adjusted for age, sex, and socio-economic status. Our findings show that, across Manitoba, overall rates of readmissions ending in death are slowly increasing, and increasing more dramatically among northern First Nations, larger First Nations not affiliated with Tribal Councils, and in the western region of the province. These regions have continuously been highlighted as disadvantaged in terms of access to care, suggesting that the time for action is overdue. Rising rates of readmissions for ACSC ending in death suggest that greater attention should be placed on access to responsive primary healthcare. These findings have broader implications for territorial healthcare systems which purchase acute care services from provinces south of them. As an indicator of quality, monitoring readmissions ending in death could provide territorial governments insights into the quality of care provided to their constituents by provincial authorities.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".