Development of an Evaluation Framework Suitable for Assessing Humanitarian Workforce Competencies During Crisis Simulation Exercises
Bibliographic record
Abstract
Abstract The need to provide a professionalization process for the humanitarian workforce is well established. Current competency-based curricula provided by existing academically affiliated training centers in North America, the United Kingdom, and the European Union provide a route toward certification. Simulation exercises followed by timely evaluation is one way to mimic the field deployment process, test knowledge of core competences, and ensure that a competent workforce can manage the inevitable emergencies and crises they will face. Through a 2011 field-based exercise that simulated a humanitarian crisis, delivered under the auspices of the World Health Organization (WHO), a competency-based framework and evaluation tool is demonstrated as a model for future training and evaluation of humanitarian providers. Cranmer H , Chan J , Kayden S , Musani A , Gasquet P , Walker P , Burkle F , Johnson K . Development of an evaluation framework suitable for assessing humanitarian workforce competencies during crisis simulation exercises . Prehosp Disaster Med . 2014 ; 29 ( 1 ): 1 - 6 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.093 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".