Factor analytic structure of the Impact of Events Scale – Revised when used with a firefighting sample
Bibliographic record
Abstract
Purpose The Impact of Events Scale (IES)/Impact of Events Scale – Revised (IES‐R) is arguably one of the most well known tools used to assess post‐traumatic symptomatology. The background literature reveals a significant gap with respect to the structural properties of the IES/IES‐R when used with emergency service populations. In response to these identified gaps, this paper aims to provide an evaluation of the structural properties of the three‐factor IES‐R when used specifically with a firefighting sample. Design/methodology/approach Responses to IES‐R for a sample of paid‐professional firefighters ( n =94) and a sample of similar comparison participants ( n =91) were evaluated for support of the suggested IES‐R subscales – i.e. hyperarousal, avoidance and intrusions. Responses for both groups were entered into a three‐factor maximum likelihood factor analysis with direct oblimin rotation. Findings The results provide further support for the use of these subscales when the IES‐R is employed with a community sample. However, the factor structure for the three suggested subscales was not supported for the firefighters' responses. With the firefighting sample, some items for the intrusion subscale loaded as expected, but no discernible pattern was evident for the hyperarousal or avoidance subscales. Given the lack of support for a three‐factor structure with the firefighting sample, scree plot analysis was used to suggest that a two‐factor solution may provide the best fit solution. The two‐factor solution revealed a construct described as general “post‐traumatic symptomatology” and a potential second factor described as “sleep”. Originality/value The author suggests a conservative approach to using the IES‐R with first responder samples, in particular firefighters, and recommends the use of an overall score in place of subscale scores. This recommendation is suggested as a temporary approach until additional research can be completed to further evaluate the present lack of support for the three IES‐R subscales when used with a firefighting sample.
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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 | 0.000 |
| 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) | 0.004 | 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".