Practical guidelines for online Narrative Exposure Therapy (e-NET) – a short-term treatment for posttraumatic stress disorder adapted for remote delivery
Bibliographic record
Abstract
Background: Online therapy has become increasingly desirable and available in recent years, with the current COVID-19 pandemic acting as a catalyst to develop further protocols enabling therapists to conduct online treatment safely and efficaciously. Offering online treatment potentially means that treatments are available to clients who would otherwise have no access, closing the gap in the provision of mental health services worldwide.Objective: This paper focuses on practical guidelines using online Narrative Exposure Therapy (e-NET). It aims to be an addition to the general manual of NET to enable therapists to deliver online treatment. The face-to-face version of NET is a well-known short-term and evidence-based treatment for posttraumatic stress disorder; e-NET is currently being tested in several additional trials.Methods: The differences between NET and e-NET are elaborated and depicted in detail.Results: Difficulties encountered in e-NET delivery, e.g. confidentiality, dealing with interruptions, comorbid symptoms among others, are similar to those that occur during face to face interventions but the solutions have to be adapted. Dissociation is often regarded as a challenge in face-to-face treatment, and requires particular attention within the online setting. Therefore, tools for addressing dissociation in this particular setting are presented.Conclusions: These practical guidelines show the advantages as well as the challenges therapists face when conducting e-NET. They aim to empower therapists working with trauma clients to conduct e-NET confidently and safely.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".