Online narrative exposure therapy for parents of children with neurodevelopmental disabilities suffering from posttraumatic stress symptoms – study protocol of a randomized controlled trial
Bibliographic record
Abstract
Background Parents of children with intellectual and neurodevelopmental disorders (IDD) often experience traumatic events in the care of their children. This leads to comparatively high numbers of mental health problems such as posttraumatic stress disorder (PTSD) in those parents. Intervention approaches for parents of children with IDD are scarce and many parents remain without support.Objective This study aims to test the feasibility and efficacy of online Narrative Exposure Therapy (eNET) with parents of children with IDD.Methods The study follows a randomized waitlist-control design. eNET is an exposure-based PTSD intervention and includes 8–12 90-minute sessions. All sessions will be conducted via video calls with trained paraprofessionals. We aim to include 50 parents, approximately 25 in the immediate intervention group and 25 in the waitlist group. Waitlist participants will receive the same intervention after a three-month wait period. All participants need to either fulfill full or subclinical PTSD symptoms according to DSM-5. Feasibility and efficacy of the intervention will be measured with pre, post, and 2 and 6 months follow-up surveys focusing on PTSD symptoms. Secondary outcomes include other health-related outcomes such as physical symptoms, depression symptoms, anxiety symptoms and functionality.Conclusions The proposed study allows us to test the feasibility and efficacy of eNET in a sample of parents of children with IDD. There are so far no published studies on the evidence of eNET; this study is one of the first randomized controlled trials investigating the feasibility and efficacy of eNET and therefore will have implications on further research and practice.Clinical trial registration: NCT04385927Date and version identifier: 22 July 2021
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".