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Record W4414259809 · doi:10.1080/28324765.2025.2555229

An exploratory study of patient preference-based sequential CBT intervention for PTSD, insomnia and nightmares after sexual assault

2025· article· en· W4414259809 on OpenAlexaff
Geneviève Belleville, Jessica Lebel

Bibliographic record

VenueCogent Mental Health · 2025
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNightmareIntervention (counseling)DistressInsomniaAnxietyExploratory researchDepression (economics)Brief intervention

Abstract

fetched live from OpenAlex

Sexual assaults have multiple-interrelated consequences, including post-traumatic stress disorder (PTSD) symptoms, insomnia and recurrent nightmares. Although these symptoms may co-vary, they often require distinct clinical attention. Treating each disorder in a sequential way may be beneficial, but no evidence supports the choice of one sequence over another. Allowing participants to choose the order of treatment may increase engagement and reduce attrition. This study explores the efficacy of a sequential intervention combining three cognitive-behaviour treatment (CBT) protocols (for PTSD, insomnia and recurrent nightmares) allowing participants to choose the order. The aims of the study were to document the overall intervention efficacy and to evaluate the differential outcome of the chosen sequences. Fourteen participants suffering from PTSD, comorbid insomnia disorder and recurrent nightmares were recruited. Results showed decreases in PTSD, insomnia, depression and anxiety symptoms and nightmare distress after the intervention. Participants targeting PTSD first did not seem to benefit more from the intervention than those targeting sleep difficulties first. All participants who completed the intervention were in PTSD remission at post-treatment, and 90% at 12-month follow-up. These exploratory findings support the feasibility of sequentially addressing PTSD, insomnia and nightmares, and provide evidence for selecting treatment sequence according to patient preferences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.132
GPT teacher head0.442
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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