Making Disaster Care Count: Consensus Formulation of Measures of Effectiveness for Natural Disaster Acute Phase Medical Response
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,328 | 0,492 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,006 |
| Bibliométrie | 0,021 | 0,010 |
| Études des sciences et des technologies | 0,005 | 0,009 |
| Communication savante | 0,010 | 0,012 |
| Science ouverte | 0,006 | 0,015 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».