Need-based resource allocation: different need indicators, different results?
Bibliographic record
Abstract
BACKGROUND: A key policy objective in most publicly financed health care systems is to allocate resources according to need. Many jurisdictions implement this policy objective through need-based allocation models. To date, no gold standard exists for selecting need indicators. In the absence of a gold standard, sensitivity of the choice of need indicators is of concern. The primary objective of this study was to assess the consistency and plausibility of estimates of per capita relative need for health services across Canadian provinces based on different need indicators. METHODS: Using the 2000/2001 Canadian Community Health Survey, we estimated relative per capita need for general practitioner, specialist, and hospital services by province using two approaches that incorporated a different set of need indicators: (1) demographics (age and sex), and (2) demographics, socioeconomic status, and health status. For both approaches, we first fitted regression models to estimate standard utilization of each of three types of health services by indicators of need. We defined the standard as average levels of utilization by needs indicators in the national sample. Subsequently, we estimated expected per capita utilization of each type of health services in each province. We compared these estimates of per capita relative need with premature mortality in each province to check their face validity. RESULTS: Both approaches suggested that expected relative per capita need for three services vary across provinces. Different approaches, however, yielded different and inconsistent results. Moreover, provincial per capita relative need for the three health services did not always indicate the same direction of need suggested by premature mortality in each province. In particular, the two approaches suggested Newfoundland had less need than the Canadian average for all three services, but it had the highest premature mortality in Canada. CONCLUSION: Substantial differences in need for health care may exist across Canadian provinces, but the direction and magnitude of differences depend on the need indicators used. Allocations from models using survey data lacked face validity for some provinces. These results call for the need to better understand the biases that may result from the use of survey data for resource allocation.
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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 | 0.153 | 0.314 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".