Contextualization of psychological treatments for government health systems in low-resource settings: group interpersonal psychotherapy for caregivers of children with nodding syndrome in Uganda
Bibliographic record
Abstract
BACKGROUND: Evidence for the effectiveness of psychological treatments in low- and middle-income countries is increasing. However, there is a lack of systematic approaches to guide implementation in government health systems. The objective of this study was to address this gap by employing the Replicating Effective Programs (REP) framework to guide contextualization of a psychological treatment in the Uganda public health system for caregivers of children affected by nodding syndrome, a neuropsychiatric disorder endemic to Sub-Saharan Africa associated with high morbidity and disability. METHODS: To contextualize a psychological treatment, we followed the four components of the REP framework: pre-conditions, pre-implementation, implementation, and maintenance and evolution. A three-step process involved reviewing health services available for nodding syndrome-affected families and current evidence for psychological treatments, qualitative formative research, and analysis and documentation of implementation activities. Stakeholders included members of affected communities, health care workers, therapists, local government leaders, and Ministry of Health officials. Detailed written, audio, and video documentation of the implementation activities was used for content analysis. RESULTS: During the pre-condition component of REP, we selected group interpersonal therapy (IPT-G) because of its feasibility, acceptability, effectiveness in the local setting, and availability of locally developed training materials. During the pre-implementation component, we adapted the training, logistics, and technical assistance strategies in conjunction with government and stakeholder working groups. Adaptations included content modification based on qualitative research with caregivers of children with nodding syndrome. During the implementation component, training was shortened for feasibility with government health workers. Peer-to-peer supervision was selected as a sustainable quality assurance method. IPT-G delivered by community health workers was evaluated for fidelity, patient outcomes, and other process-level variables. More than 90% of beneficiaries completed the treatment program, which was effective in reducing caregiver and child mental health problems. With the Ministry of Health, we conducted preparatory activities for the maintenance and evolution component for scale-up throughout the country. CONCLUSIONS: The REP framework provides a systematic approach to guide contextualization of psychological treatments for delivery in low-resource public health systems. Specific recommendations are provided for REP's application in global mental health. TRIAL REGISTRATION: ISRCTN11382067 ; 08/06/2016; retrospectively registered.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".