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Record W2394967657 · doi:10.1080/17522439.2016.1167946

Psychological interventions for trauma in individuals who have psychosis: A systematic review and meta-analysis

2016· review· en· W2394967657 on OpenAlexaff
Jacqueline Sin, Debbie Spain

Bibliographic record

VenuePsychosis · 2016
Typereview
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsPopulation Health Research Institute
Fundersnot available
KeywordsHypervigilancePsychologyPsychological interventionClinical psychologyAnxietyPopulationPsychosisMoodDistressMeta-analysisPsychiatryPsychotherapistMedicine

Abstract

fetched live from OpenAlex

Background: Psychological interventions, in particular those derived from cognitive-behavioural therapy frameworks, and eye movement desensitisation and reprocessing, are effective for reducing post-traumatic stress disorder and associated distress. To date, studies have tended to exclude individuals who have psychosis; a clinical population who are known to be at risk of experiencing trauma. Whether people with psychosis also benefit from trauma-focussed psychological therapies (TFPT) warrants further investigation.Method: A systematic search for randomised controlled trials was undertaken. Data were synthesised using narrative and meta-analytic approaches.Results: Five studies met the review inclusion criteria. Study findings overall indicate that TFPT are effective for reducing intrusive thoughts and images, negative beliefs associated with traumatic memories, hypervigilance, and avoidance. Limited data were available about the utility of interventions for improving mood, anxiety and quality of life. Attrition rates were comparable for participants offered active and control conditions.Conclusion: Findings are consistent with those reported for the non-psychosis population. Future studies should establish which interventions are more acceptable and glean more favourable outcomes for this clinical population.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.505
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0180.012
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.502
GPT teacher head0.567
Teacher spread0.064 · 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; both teacher heads agree on what is shown here.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations63
Published2016
Admission routes1
Has abstractyes

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