Modeling Psychosocial Decision Making in Emergency Operations Centres
Bibliographic record
Abstract
The researchers compared the effectiveness of two decision models for modeling decision making in Emergency Operations Centers (EOCs): Klein's Recognition Primed Decision (RPD) model and Gladwin's Ethnographic Decision Tree Model (EDTM). The focus was on decisions that affect the psychological and social well-being of responders and community members. Communities of EOC personnel participated in a simulated emergency event, followed by an interview and/or focus group. Analysis of the decision-making processes during the simulation revealed that most operational decisions were made intuitively, with expertise, and best modeled by RPD. When the decisions involved issues for which EOC personnel had less experience (e.g., psychosocial issues), the decision-making approach shifted from a fast intuitive style to a more deliberative style. In some cases, EOC staff requested additional information before making a decision. With no formalized feedback loops, decisions were delayed or not made at all, leaving community residents and EOC personnel without psychosocial services for unnecessary lengths of time. The researchers found the RPD model to be most useful in its potential for identifying areas where future training (i.e., simulated exercises) and education (i.e., knowledge transfer) could be offered to EOC personnel to improve the provision of psychosocial services.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".