Making Disaster Care Count: Consensus Formulation of Measures of Effectiveness for Natural Disaster Acute Phase Medical Response
Bibliographic record
Abstract
INTRODUCTION: No standard exists for provision of care following catastrophic natural disasters. Host nations, funders, and overseeing agencies need a method to identify the most effective interventions when allocating finite resources. Measures of effectiveness are real-time indicators that can be used to link early action with downstream impact. HYPOTHESIS: Group consensus methods can be used to develop measures of effectiveness detailing the major functions of post natural disaster acute phase medical response. METHODS: A review of peer-reviewed disaster response publications (2001-2011) identified potential measures describing domestic and international medical response. A steering committee comprised of six persons with publications pertaining to disaster response, and those serving in leadership capacity for a disaster response organization, was assembled. The committee determined which measures identified in the literature review had the best potential to gauge effectiveness during post-disaster acute-phase medical response. Using a modified Delphi technique, a second, larger group (Expert Panel) evaluated these measures and novel measures suggested (or "free-texted") by participants for importance, validity, usability, and feasibility. After three iterations, the highest rated measures were selected. RESULTS: The literature review identified 397 measures. The steering committee approved 116 (29.2%) of these measures for advancement to the Delphi process. In Round 1, 25 (22%) measures attained >75% approval and, accompanied by 77 free-text measures, graduated to Round 2. There, 56 (50%) measures achieved >75% approval. In Round 3, 37 (66%) measures achieved median scores of 4 or higher (on a 5-point ordinal scale). These selected measures describe major aspects of disaster response, including: Evaluation, Treatment, Disposition, Public Health, and Team Logistics. Of participants from the Expert Panel, 24/39 (63%) completed all rounds. Thirty-three percent of these experts represented international agencies; 42% represented US government agencies. CONCLUSION: Experts identified response measures that reflect major functions of an acute medical response. Measures of effectiveness facilitate real-time assessment of performance and can signal where practices should be improved to better aid community preparedness and response. These measures can promote unification of medical assistance, allow for comparison of responses, and bring accountability to post-disaster acute-phase medical care. This is the first consensus-developed reporting tool constructed using objective measures to describe the functions of acute phase disaster medical response. It should be evaluated by agencies providing medical response during the next major natural disaster.
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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 | 0.328 | 0.492 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.010 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".