The Need for a Systematic Approach to Disaster Psychosocial Response: A Suggested Competency Framework
Bibliographic record
Abstract
Competency models attempt to define what makes expert performers "experts." Successful disaster psychosocial planning and the institutionalizing of psychosocial response within emergency management require clearly-defined skill sets. This necessitates anticipating both the short- and long-term psychosocial implications of a disaster or health emergency (ie, pandemic) by developing effective and sustained working relationships among psychosocial providers, programs, and other planning partners. The following article outlines recommended competencies for psychosocial responders to enable communities and organizations to prepare for and effectively manage a disaster response. Competency-based models are founded on observable performance or behavioral indicators, attitudes, traits, or personalities related to effective performance in a specific role or job. After analyzing the literature regarding competency-based frameworks, a proposed competency framework that details 13 competency domains is suggested. Each domain describes a series of competencies and suggests behavioral indicators for each competency and, where relevant, associated training expectations. These domains have been organized under three distinct categories or types of competencies: general competency domains; disaster psychosocial intervention competency domains; and disaster psychosocial program leadership and coordination competency domains. Competencies do not replace job descriptions nor should they be confused with performance assessments. What they can do is update and revise job descriptions; orient existing and new employees to their disaster/emergency roles and responsibilities; target training needs; provide the basis for ongoing self-assessment by agencies and individuals as they evaluate their readiness to respond; and provide a job- or role-relevant basis for performance appraisal dimensions or standards and review discussions. Using a modular approach to psychosocial planning, service providers can improve their response capacity by utilizing differences in levels of expertise and training. The competencies outlined in this paper can thus be used to standardize expectations about levels of psychosocial support interventions. In addition this approach provides an adaptable framework that can be adjusted for various contexts.
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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 | 0.107 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.005 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".