An Initial Investigation of the Factor-Analytic Structure of the Impact of Event Scale-Revised With a Volunteer Firefighter Sample
Bibliographic record
Abstract
We provide an initial evaluation of the factor structure of the Impact of Event Scale-Revised (IES-R) when used with a volunteer firefighter and a similar community participant sample. A volunteer firefighter sample (n = 65) and a sample of similar community respondents (n = 103) completed a questionnaire study, including responses to the IES-R. The IES-R data from both groups were entered into a three-factor principal components analysis with direct oblimin rotation. We found further support for the validity of the IES-R when used with a community sample. However, our data suggested that when using the IES-R with a community sample, the choice between a two- and a three-factor model may depend on the composition of the participants. For volunteer firefighters, the factor-analytic structure of the IES-R appeared to be similar to that of the community sample, with more scatter in terms of item loadings. To our knowledge, there is no previous research considering the use of the IES-R with a strictly volunteer firefighter sample. In addition, despite adequate research on the factor-analytic structure of the original IES, little research has considered the factor-analytic structure of the more recent IES-R, even with community samples.
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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) | 0.000 | 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".