Does technique matter; a pilot study exploring weighting techniques for a multi-criteria decision support framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There is an increased interest in the use of multi-criteria decision analysis (MCDA) to support regulatory and reimbursement decision making. The EVIDEM framework was developed to provide pragmatic multi-criteria decision support in health care, to estimate the value of healthcare interventions, and to aid in priority-setting. The objectives of this study were to test 1) the influence of different weighting techniques on the overall outcome of an MCDA exercise, 2) the discriminative power in weighting different criteria of such techniques, and 3) whether different techniques result in similar weights in weighting the criteria set proposed by the EVIDEM framework. METHODS: A sample of 60 Dutch and Canadian students participated in the study. Each student used an online survey to provide weights for 14 criteria with two different techniques: a five-point rating scale and one of the following techniques selected randomly: ranking, point allocation, pairwise comparison and best worst scaling. RESULTS: The results of this study indicate that there is no effect of differences in weights on value estimates at the group level. On an individual level, considerable differences in criteria weights and rank order occur as a result of the weight elicitation method used, and the ability of different techniques to discriminate in criteria importance. Of the five techniques tested, the pair-wise comparison of criteria has the highest ability to discriminate in weights when fourteen criteria are compared. CONCLUSIONS: When weights are intended to support group decisions, the choice of elicitation technique has negligible impact on criteria weights and the overall value of an innovation. However, when weights are used to support individual decisions, the choice of elicitation technique influences outcome and studies that use dissimilar techniques cannot be easily compared. Weight elicitation through pairwise comparison of criteria is preferred when taking into account its superior ability to discriminate between criteria and respondents' preferences.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it