Rasch analysis of the firefighters’ critical incident inventory questionnaire
Bibliographic record
Abstract
BACKGROUND: The Critical Incident Inventory (CII) was developed to assess stressful exposures in firefighters and emergency service workers. The CII includes six subscales: trauma to self, victims known to fire-emergency worker, multiple casualties, incidents involving children, unusual or problematic tactical operations, and exposure to severe medical trauma. OBJECTIVES: To examine the construct validity of all subscales of the Critical Incident Inventory (CII) by assessing the unidimensionality of the scales, and the interval properties of CII subscales by examining fit to the Rasch model and ordering of item thresholds. METHODS: This was a secondary data analysis based on survey data collected from a sample of 390 firefighters. RESULTS: Item 4 and Item 20 were removed with the confirmation of unacceptable fit residual. This revised version of the CII showed satisfactory fit to the Rasch model by non-significant Chi-square test and acceptable level of item fit. We rescored the CII original version and considered all items as only dichotomous response options where 0 represented the original no experience, and 1 presents the combination of experiencing 1, 2, 3 cases. CONCLUSION: The re-appraisal of the revised version CII indicated a satisfactory level of Rasch model fit.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".