Nonclinical Core Competencies and Effects of Interprofessional Teamwork in Disaster and Emergency Response Training and Practice: A Pilot Study
Bibliographic record
Abstract
OBJECTIVE: To define and delineate the nontechnical core competencies required for disaster response, Disaster Medical Assistance Team (DMAT) members were interviewed regarding their perspectives and experiences in disaster management. Also explored was the relationship between nontechnical competencies and interprofessional collaboration. METHODS: In-depth interviews were conducted with 10 Canadian DMAT members to explore how they viewed nontechnical core competencies and how their experiences influenced their perceptions toward interprofessonalism in disaster response. Data were examined using thematic analysis. RESULTS: Nontechnical core competencies were categorized under austere skills, interpersonal skills, and cognitive skills. Research participants defined interprofessionalism and discussed the importance of specific nontechnical core competencies to interprofessional collaboration. CONCLUSIONS: The findings of this study established a connection between nontechnical core competencies and interprofessional collaboration in DMAT activities. It also provided preliminary insights into the importance of context in developing an evidence base for competency training in disaster response and management. (Disaster Med Public Health Preparedness. 2013;0:1-8).
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".