The devil is in the details: trends in avoidable hospitalization rates by geography in British Columbia, 1990–2000
Bibliographic record
Abstract
BACKGROUND: Researchers and policy makers have focussed on the development of indicators to help monitor the success of regionalization, primary care reform and other health sector restructuring initiatives. Certain indicators are useful in examining issues of equity in service provision, especially among older populations, regardless of where they live. AHRs are used as an indicator of primary care system efficiency and thus reveal information about access to general practitioners. The purpose of this paper is to examine trends in avoidable hospitalization rates (AHRs) during a period of time characterized by several waves of health sector restructuring and regionalization in British Columbia. AHRs are examined in relation to non-avoidable and total hospitalization rates as well as by urban and rural geography across the province. METHODS: Analyses draw on linked administrative health data from the province of British Columbia for 1990 through 2000 for the population aged 50 and over. Joinpoint regression analyses and t-tests are used to detect and describe trends in the data. RESULTS: Generally speaking, non-avoidable hospitalizations constitute the vast majority of hospitalizations in a given year (i.e. around 95%) with AHRs constituting the remaining 5% of hospitalizations. Comparing rural areas and urban areas reveals that standardized rates of avoidable, non-avoidable and total hospitalizations are consistently higher in rural areas. Joinpoint regression results show significantly decreasing trends overall; lines are parallel in the case of avoidable hospitalizations, and lines are diverging for non-avoidable and total hospitalizations, with the gap between rural and urban areas being wider at the end of the time interval than at the beginning. CONCLUSION: These data suggest that access to effective primary care in rural communities remains problematic in BC given that rural areas did not make any gains in AHRs relative to urban areas under recent health sector restructuring initiatives. It remains important to continue to monitor the discrepancy between them as a reflection of inequity in service provision. In addition, it is important to consider alternative explanations for the observed trends paying particular attention to the needs of rural and urban populations and the factors influencing local service provision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".