Le regroupement des hôpitaux selon leur production : base d’évaluation de leur performance
Bibliographic record
Abstract
The review of the budgetary bases of hospitals in Quebec is conducted in three stages: (1) classifying hospitals; (2) evaluating the performance of human and material resources used in each hospital as well as comparing this performance with the average performance of the peer-groups; (3) using the results to establish a correcting mechanism for an adequation redistribution of a portion of the total budget among individual hospitals. The realism of this process of budgetary review lies in the possibility of appropriately classifying the hospitals according to their output. In this paper, along with a short presentation, at the end, of the two last steps of the review process, we focus on the classification itself. We define firstly what constitutes the hospital's output, which is a mix of inpatient services (main variable), outpatient services, research and teaching, as well as environmental variables influencing the output. Then we describe the classification technique, which uses two different similarity indices, one for the distribution of patient-days by category of diagnosis (ICDA, 8th revision), and the other for all other variables; these indices are incorporated in a hierarchical sorting strategy based on the optimization of an objective function. This method is subsequently applied to the acute short-term hospitals of Quebec, using 1976-77 data, and the different resulting eight groups of hospitals are broadly described. In the conclusion, are indicated some suggestions for improving the classification and the budget reviewing mechanism itself.
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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 | 0.041 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".