PHP139 Which Criteria are Considered in Health Care Decisions? Insights from an International Survey of Policy and Clinical Decision Makers
Bibliographic record
Abstract
Defining and applying decision criteria are key to accountability and reasonableness in decisionmaking and to ensure the most beneficial allocation of health care resources. Objectives were to gather data on criteria considered by policy and clinical decisionmakers in health care decisions globally. An online questionnaire was developed with 43 criteria organized into 10 clusters. Using snowball sampling, decision makers were invited by an international task force to report which criteria they consider and how these criteria weighed when making decisions on health care interventions in their context. For each criterion, respondents reported “currently considered”, “should be considered” and weight assigned. Differences in proportions of participants reporting consideration of each criterion were explored with inferential statistics across levels of decision (micro, meso, macro), decision-maker perspective, and world region. A total of 140 decision makers (1/3 clinical, 2/3 policy) from 23 countries in five continents completed the survey. Most relevant criteria (top ranked for “Currently considered”, “Should be considered” and weights) were Clinical efficacy/effectiveness, Safety, Quality of evidence, Disease severity and Impact on health care costs. Organizational and skill requirements ranked high for consideration but low for weights. For a majority of criteria, the number of decision makers reporting that they ‘should be considered' was higher than that reporting they are currently considered (P<0.05). For more than 75% of criteria, there were no statistical differences in proportions across levels of decision, perspective and world region. Differences in proportions across several comparisons were statistically significant for criteria: Population priorities, Stakeholder pressure/interests, Capacity to stimulate research, Impact on partnership and collaboration, and Environmental impact (P<0.05). Results suggest a significant international agreement among decision makers on the relevance of a core set of normative and feasibility criteria and on the need to consider a wider range of criteria in decision-making processes. Areas of divergence appear to be principally related to contextual aspects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".