Task sharing for the care of severe mental disorders in a low-income country (TaSCS): study protocol for a randomised, controlled, non-inferiority trial
Bibliographic record
Abstract
Task sharing mental health care through integration into primary health care (PHC) is advocated as a means of narrowing the treatment gap for mental disorders in low-income countries. However, the effectiveness, acceptability, feasibility and sustainability of this service model for people with a severe mental disorder (SMD) have not been evaluated in a low-income country.A randomised, controlled, non-inferiority trial will be carried out in a predominantly rural area of Ethiopia. A sample of 324 people with SMD (diagnoses of schizophrenia, schizoaffective disorder, bipolar disorder or major depressive disorder) with an ongoing need for mental health care will be recruited from 1) participants in a population-based cohort study and 2) people attending a psychiatric nurse-led out-patient clinic. The intervention is a task-sharing model of locally delivered mental health care for people with SMD integrated into PHC delivered over 18 months. Participants in the active control arm will receive the established and effective model of specialist mental health care delivered by psychiatric nurses at an out-patient clinic within a centrally located general hospital. The hypothesis is that people with SMD who receive mental health care integrated into PHC will have a non-inferior clinical outcome, defined as a mean symptom score on the Brief Psychiatric Rating Scale, expanded version, of no more than six points higher, compared to participants who receive the psychiatric nurse-led service, after 12 months. The primary outcome is change in symptom severity. Secondary outcomes are functional status, relapse, service use costs, service satisfaction, drop-out and medication adherence, nutritional status, physical health care, quality of care, medication side effects, stigma, adverse events and cost-effectiveness. Sustainability and cost-effectiveness will be further evaluated at 18 months. Randomisation will be stratified by health centre catchment area using random permuted blocks. The outcome assessors and investigators will be masked to allocation status.Evidence about the effectiveness of task sharing mental health care for people with SMD in a rural, low-income African country will inform the World Health Organisation's mental health Gap Action Programme to scale-up mental health care globally.NCT02308956 (ClinicalTrials.gov). Date of registration: 3 December 2014.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.009 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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 source (direct Gemma or distilled Codex), 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".