Setting priorities in healthcare institutions: The case of McGill University Health Centre
Bibliographic record
Abstract
Priority setting is a decision-making process concerning the distribution of resources. The imbalance between allocated resources and public demand for health services as well as the inherent complexity of healthcare institutions are making priority setting one of the most challenging health management issues. Nevertheless, the priority setting processes and policymaking have not been studied very much at the hospital strategic planning level, i.e., the prioritisation of clinical activities. The purpose of this paper is to provide an evidence based case for improving the priority setting process in large hospitals. In a qualitative case study carried out at the McGill University Health Centre (MUHC), a priority setting exercise is described and the process is assessed in line with an accountability for reasonableness framework. Data collection involved in-depth, one-on-one interviews with key participants, review of key documents, and in-field observation. To assess the priority setting exercise, this paper compares the priority setting process against the five conditions of accountability for reasonableness, and identifies good practices and opportunities for improvement.
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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 | 0.029 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.021 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".