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Record W4399803082 · doi:10.1016/j.ajp.2024.104120

Pilot study to test the feasibility and clinical efficacy of a psychosocial care programme for patients with psychosis in low-resource settings

2024· article· en· W4399803082 on OpenAlexaff
Mamta Sood, Rakesh Kumar Chadda, Pushpendra Singh, Nishtha Chawla, Rekha Patel, Vaibhav Patil, R. Thara, Mohapradeep Mohan, Srividya N. Iyer, Jai Shah, Jason Madan, Max Birchwood, Caroline Meyer, Richard Lilford, Vivek Furtado, Graeme Currie, Swaran P. Singh

Bibliographic record

VenueAsian Journal of Psychiatry · 2024
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersAll-India Institute of Medical SciencesUniversity of WarwickNational Institute for Health and Care Research
KeywordsPsychosocialIntervention (counseling)MedicineCaregiver burdenPopulationPsychopathologySchizophrenia (object-oriented programming)PsychiatryClinical psychologyPsychologyDementia

Abstract

fetched live from OpenAlex

BACKGROUND: Home-based psychosocial care has the potential to improving outcomes in patients with schizophrenia and related disorders (SCZ). There is lack of India data for such care in early psychosis. We developed the "Saksham" programme, a bespoke self-managed home-based psychosocial care model, available in two formats: manual-based and mobile-application based. With the anticipated success of recruitment of early psychosis cases in our setting, we plan to test the such intervention in this population in future trials. AIM: To assess the feasibility of the Saksham programme intervention in people with SCZ and its clinical efficacy as an adjunct to treatment as usual. METHODS: Seventy-five patient-caregiver pairs (total n=150) were recruited. Patients received either: treatment-as-usual (TAU) (n=25), manual-based Saksham intervention+TAU (n=25), or app-based Saksham intervention+TAU (n=25). Feasibility (i.e. acceptability, practicality, demand, implementation and integration) was assessed at three-months. Participants were assessed for psychopathology, illness-severity, cognition, functioning, disability, and caregiver-coping at baseline, one-month, and three-month. The percentage changes over time were compared across three groups. RESULTS: More found the mobile application-based intervention acceptable and easy-to-use than the manual-based intervention (92 % vs 68 %, and 76 % vs 68 %, respectively). Psychopathology and caregiver-burden improved significantly in all three groups (p<0.05). Cognition, disability, functioning, and caregiver burden improved significantly in the two Saksham intervention groups, with greater improvement in the Saksham app group (p<0.05). CONCLUSION: Home-based intervention is feasible and acceptable in a low-resource setting, with preliminary evidence for effectiveness. These findings need corroboration with randomised controlled trials in early psychosis to ameliorate course of illness.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.030
GPT teacher head0.368
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations3
Published2024
Admission routes1
Has abstractno

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