Experimental Measures of Output and Productivity in the Canadian Hospital Sector, 2002 to 2010
Bibliographic record
Abstract
Recent discussions about health care spending have focused on two issues: 1) the extent to which the increase in heath care spending is due to an increase in the quantity as opposed to the price of health care services, and 2) the efficiency and productivity of health care providers (e.g., hospital sectors, office of physicians, and long-term care). The key to addressing both issues is a direct output measure of health care services?a measure that does not currently exist. In the National Accounts, output of the health care sector is measured by the volume of inputs, which includes labour costs for physicians, nurses and administrative staff, consumption of capital, and intermediate inputs. An input-based output measure assumes that there are no productivity gains in the health care sector. As a result, it does not provide a measure of productivity performance, nor does it allow a decomposition of total health care expenditures into price and output quantity components. The main objective of this paper is to develop an experimental direct output measure for the Canadian hospital sector that can be used to address those issues. A large number of countries have already constructed a direct output measure of the hospital sector and other healthcare sectors.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".