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Record W1990558141 · doi:10.1111/tmi.12500

Psychological interventions for Common Mental Disorders for People Living With <scp>HIV</scp> in Low‐ and Middle‐Income Countries: systematic review

2015· review· en· W1990558141 on OpenAlexfundno aff
Dixon Chibanda, Frances M. Cowan, Jessica L. Healy, Melanie Abas, Crick Lund

Bibliographic record

VenueTropical Medicine & International Health · 2015
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersGrand Challenges Canada
KeywordsPsychological interventionMedicineMental healthFidelitySystematic reviewInclusion (mineral)Clinical psychologyPsychiatryHuman immunodeficiency virus (HIV)MEDLINEFamily medicinePsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the effectiveness of structured psychological interventions against common mental disorders (CMD) in people living with HIV infection (PLWH), in low- and middle-income countries (LMIC). METHODS: Systematic review of psychological interventions for CMD from LMIC for PLWH, with two-stage screening carried out independently by 2 authors. RESULTS: Of 190 studies, 5 met inclusion criteria. These were randomised-controlled trials based on the principles of cognitive behaviour therapy (CBT) and were effective in reducing CMD symptoms in PLWH. Follow-up of study participants ranged from 6 weeks to 12 months with multiple tools utilised to measure the primary outcome. Four studies showed a high risk of bias, while 1 study from Iran met low risk of bias in all 6 domains of the Cochrane risk of bias tool and all 22 items of the CONSORT instrument. CONCLUSION: There is a need for more robust and adequately powered studies to further explore CBT-based interventions in PLWH. Future studies should report on components of the psychological interventions, fidelity measurement and training, including supervision of delivering agents, particularly where lay health workers are the delivering agent.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.103
GPT teacher head0.479
Teacher spread0.376 · 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.

Study designSystematic review
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

Citations86
Published2015
Admission routes1
Has abstractyes

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