WHICH CRITERIA ARE CONSIDERED IN HEALTHCARE DECISIONS? INSIGHTS FROM AN INTERNATIONAL SURVEY OF POLICY AND CLINICAL DECISION MAKERS
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to gather qualitative and quantitative data on criteria considered by healthcare decision makers. METHODS: Using snowball sampling and an online questionnaire with forty-three criteria organized into ten clusters, decision makers were invited by an international task force to report which criteria they consider when making decisions on healthcare interventions in their context. Respondents reported whether each criterion is "currently considered," "should be considered," and its relative weight (scale 0-5). Differences in proportions of respondents were explored with inferential statistics across levels of decision (micro, meso, macro), decision maker perspectives, and world regions. RESULTS: A total of 140 decision makers (1/3 clinical, 2/3 policy) from 23 countries in five continents completed the survey. The 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 healthcare costs. Organizational and skill requirements were frequently considered but had relatively low weights. For almost all criteria, a higher proportion of decision makers reported that they "Should be considered" than that they are "Currently considered" (p < .05). For more than 74 percent of criteria, there were no statistical differences in proportions across levels of decision, perspectives and world regions. Statistically significant differences across several comparisons were found for: Population priorities, Stakeholder pressure/interests, Capacity to stimulate research, Impact on partnership and collaboration, and Environmental impact. CONCLUSIONS: Results suggest convergence among decision makers on the relevance of a core set of criteria and on the need to consider a wider range of criteria. Areas of divergence appear to be principally related to contextual factors.
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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 | 0.062 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".