The Measurement of Output and Productivity in the Health
Bibliographic record
Abstract
To achieve efficient allocation of resources in the health care sector, accurate measures of health care output and productivity are essential. According to official estimates of productivity produced by Statistics Canada, labour productivity in the business sector of the health care (excluding hospitals) and social assistance industry declined 0.28 per cent per year between 1994 and 2003. Estimates of productivity produced by the CSLS, based on official Statistics Canada employment and real GDP figures, show that labour productivity in the health care and social assistance industry declined by 0.69 per cent per year between 1987 and 2006. It is widely recognized that official output and productivity figures may seriously underestimate the true contribution of the health care sector to real output, and more importantly to the economic well-being of Canadians. Alternative approaches show that price indices for health care output may be overestimated and, therefore, quality improvements may not be accurately captured by estimates of real health care output. More resources are needed to further investigate the alternative approaches discussed in this report and develop better output measures that
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".