Scaling up mental health service provision through multisectoral integration: A qualitative analysis of factors shaping delivery and uptake among South Sudanese refugees and healthcare workers in Uganda
Bibliographic record
Abstract
Background: There is a growing need for mental health and psychosocial support (MHPSS) interventions that can feasibly be provided to larger groups of people, particularly in humanitarian settings. However, scaling up mental health interventions is notoriously difficult. There are therefore growing calls for integrating mental health outside traditional health structures, both to increase reach and to address social determinants of mental health. The objective of this study is to explore barriers and facilitators of Self-Help Plus (SH+), an MHPSS innovation implemented through multisectoral integration. We explore delivery and uptake at the scale of SH+ and aim to understand intervention adaptation needs when integrating SH+ within other health and non-health sectors in Uganda. Method: We conducted a qualitative study using in-depth interviews in two phases: first for a needs and resource assessment, and second for a process evaluation. We conducted 50 in-depth interviews with BRAC Uganda and MoH partner staff, intervention facilitators, and target impact group members between July and December 2022. A thematic network analysis process was used to identify barriers and facilitators of SH+ delivery and uptake at scale in Uganda. Results: We identified five major factors that should be considered when scaling through multisectoral integration, namely: (1) adaptivity, (2) funding mechanisms, (3) social capital, (4) participation, and (5) sustainability. Within these factors, there were varying degrees to which a factor was a facilitator or barrier, depending on participants' perceptions of the intervention. Conclusions: Our findings suggest that multisectoral integration of SH+ into sectors both inside and outside of health may be a viable means to scale SH+ and increase reach. However, funding, partnerships, co-creation, and adaptability need to be further explored to facilitate better and more sustainable integration.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".