Triage: A new group technique gaining recognition in evaluation
Bibliographic record
Abstract
TRIAGE, or Technique for Research of Information by Animation of a Group of Experts, is an inductive and structured method for collecting information that aims to obtain a group consensus. The goal of this technique is to provide quality informative material quickly and efficiently to enable decision-making or to develop more sophisticated survey tools. TRIAGE both distinguishes itself from, and complements, the main group techniques used in evaluation up until now. These are the Delphi technique, the Nominal Group Technique (NGT) and the focus group (Delbecq, Van de Ven & Gustafson, 1975). The definition, the context for use as well as the different parts of the usual process of TRIAGE technique (recruiting of participants, individual production phase, collective production phase with visual support, validation of results) will firstly be presented then compared to these advocated in the Delphi, NGT and focus group techniques. Also, examples of TRIAGE being applied in different evaluation contexts, such as the development of measurement instruments and the evaluation of health programs, will be presented. These examples will illustrate the richness, the flexibility and the potential of this technique as an assessment tool. Finally, the strengths and shortcomings of TRIAGE will be discussed.
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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 | 0.164 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".