Provincial analysis of relative efficiency in Canadian hospitals using DEA
Bibliographic record
Abstract
Studying the efficiency of hospitals in Canada is very important for optimizing resource allocation and assuring equitable access to high-quality care in different provinces in Can-ada. The analysis could reveal issues in identifying best practices and areas of inefficiency, thereby informing policy decisions and shaping investment strategies. In this paper, we present a four data envelopment analysis methods, namely, CCR, BCC Input Oriented, BCC Output Oriented and Additive to measure the relative efficiency of 10 provinces in terms of offering different services including Discharges, Surgeries and Diagnostic Exams for their patients. The proposed study has implemented DEA technique and using three inputs, Beds in Operations, Hospital Staff (FTE) and Operating Expenses, and outputs, Dis-charges, Surgeries and Diagnostic Exams, measures the relative efficiencies for 10 provinces. The study also uses four DEA techniques for measuring the relative efficiencies of hospitals. The implementation of CCR for all four models yields lower scores compared with other methods. Overall, the method confirms that most hospitals across the country perform relatively well.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".