Setting Priorities in Healthcare at McGill University Health Centre, Canada
Bibliographic record
Abstract
The goal of this research is to present evidence-based recommendations for enhancing the priority setting process in large hospitals. Priority setting is a decision-making process that involves the allocation of resources. The disparity between available resources and public demand for health services, as well as the intrinsic complexity of healthcare organizations, make priority setting one of the most difficult health management concerns. Nonetheless, the processes of prioritizing and policymaking at the hospital strategic planning level, i.e., the prioritization of clinical activities, have received little attention. As a result, a qualitative case study at the McGill University Health Centre (MUHC) was conducted. A priority setting exercise is detailed in this paper, and the process is evaluated using an accountability for reasonableness paradigm. In-depth, one-on-one interviews with important participants, a review of significant documents, and on-the-ground observation were all used to collect data. To evaluate the priority setting process, this article compares it to the five requirements of accountability for reasonableness and identifies effective practices and areas for development. The collected information gave decision-makers the ability to effectively evaluate clinical activities and to make good decisions. Moreover, the priority setting process was perceived equitable, and there was a general satisfaction from stakeholders with the way the exercise was performed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".