Efficiency and Technological Change in Health Care Services in Ontario
Bibliographic record
Abstract
This paper presents productivity measurement results for hospital services using panel data for Ontario hospitals between 2003 and 2006. The study uses the Malmquist Productivity index (MPI) obtained through the application of Data Envelopment Analysis (DEA) which is decomposed into efficiency change (ECH), i.e., movement towards the best practice frontier and technological change (TCH), i.e., movement of the frontier itself (Fare et al. [12]). The study also uses kernel density estimation techniques for analysis of efficiency distributions of the productivity scores and their components across different types of hospitals (e.g. small /large and rural /urban) and over time. Our results suggest that in addition to average productivity it is important to examine distributions of productivity and of its components which we find differs by hospital type and over time. We find that productivity growth occurred mostly through improvement in technology and in spite of declining efficiency. The results provide useful insight into the underlying mechanisms of observed changes in overall productivity, in technological change and in technical efficiency change in this vital sector of the health care market.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".