An Electronic Competency-Based Evaluation Tool for Assessing Humanitarian Competencies in a Simulated Exercise
Bibliographic record
Abstract
METHODS: The evaluation tool was first derived from the formerly Consortium of British Humanitarian Agencies' (CBHA; United Kingdom), now "Start Network's," Core Humanitarian Competency Framework and formatted in an electronic data capture tool that allowed for offline evaluation. During a 3-day humanitarian simulation event, participants in teams of eight to 10 were evaluated individually at multiple injects by trained evaluators. Participants were assessed on five competencies and a global rating scale. Participants evaluated both themselves and their team members using the same tool at the end of the simulation exercise (SimEx). RESULTS: All participants (63) were evaluated. A total of 1,008 individual evaluations were completed. There were 90 (9.0%) missing evaluations. All 63 participants also evaluated themselves and each of their teammates using the same tool. Self-evaluation scores were significantly lower than peer-evaluations, which were significantly lower than evaluators' assessments. Participants with a medical degree, and those with humanitarian work experience of one month or more, scored significantly higher on all competencies assessed by evaluators compared to other participants. Participants with prior humanitarian experience scored higher on competencies regarding operating safely and working effectively as a team member. CONCLUSION: This study presents a novel electronic evaluation tool to assess individual performance in five of six globally recognized humanitarian competency domains in a 3-day humanitarian SimEx. The evaluation tool provides a standardized approach to the assessment of humanitarian competencies that cannot be evaluated through knowledge-based testing in a classroom setting. When combined with testing knowledge-based competencies, this presents an approach to a comprehensive competency-based assessment that provides an objective measurement of competency with respect to the competencies listed in the Framework. There is an opportunity to advance the use of this tool in future humanitarian training exercises and potentially in real time, in the field. This could impact the efficiency and effectiveness of humanitarian operations. Evans AB , Hulme JM , Nugus P , Cranmer HH , Coutu M , Johnson K . An electronic competency-based evaluation tool for assessing humanitarian competencies in a simulated exercise. Prehosp Disaster Med. 2017;32(3):253-260.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".