Paraprofessional delivery of online narrative exposure therapy for firefighters
Bibliographic record
Abstract
Firefighters are at increased risk for developing posttraumatic stress disorder (PTSD) and face numerous barriers to accessing mental health care. Innovative ways to increase access to evidence-based interventions are needed. This study was a case series testing the acceptability, feasibility, and preliminary effectiveness of a paraprofessional-delivered, virtual narrative exposure therapy (eNET) intervention for PTSD. Participants were 21 firefighters who met the criteria for clinical or subclinical probable PTSD and completed 10-12 sessions of eNET via videoconference. Participants completed self-report measures pre- and postintervention and at 2- and 6-month follow-ups as well as a postintervention qualitative interview. Paired samples t tests evidenced statistically significant decreases in PTSD, anxiety, and depressive symptom severity and functional impairment from pre- to postintervention, ds = 1.08-1.33, and in PTSD and anxiety symptom severity and functional impairment from preintervention to 6-month follow-up, ds = 0.69-1.10. The average PTSD symptom severity score fell from above to below the clinical cutoff for probable PTSD at postintervention and follow-ups. Qualitative interviews indicated that paraprofessionals were considered central to participants' success and experience with the intervention. No adverse events or safety concerns were raised. This study is an important step in demonstrating that appropriately trained and supervised paraprofessionals can effectively deliver eNET to firefighters with PTSD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".