Societal and organisational influences on implementation of mental health peer support work in low-income and high-income settings: a qualitative focus group study
Bibliographic record
Abstract
OBJECTIVES: Despite the established evidence base for mental health peer support work, widespread implementation remains a challenge. This study aimed to explore societal and organisational influences on the implementation of peer support work in low-income and high-income settings. DESIGN: Study sites conducted two focus groups in local languages at each site, using a topic guide based on a conceptual framework describing eight peer support worker (PSW) principles and five implementation issues. Transcripts were translated into English and an inductive thematic analysis was conducted to characterise implementation influences. SETTING: The study took place in two tertiary and three secondary mental healthcare sites as part of the Using Peer Support in Developing Empowering Mental Health Services (UPSIDES) study, comprising three high-income sites (Hamburg and Ulm, Germany; Be'er Sheva, Israel) and two low-income sites (Dar es Salaam, Tanzania; Kampala, Uganda) chosen for diversity both in region and in experience of peer support work. PARTICIPANTS: 12 focus groups were conducted (including a total of 86 participants), across sites in Ulm (n=2), Hamburg (n=2), Dar es Salaam (n=2), Be'er Sheva (n=2) and Kampala (n=4). Three individual interviews were also done in Kampala. All participants met the inclusion criteria: aged over 18 years; actual or potential PSW or mental health clinician or hospital/community manager or regional/national policy-maker; and able to give informed consent. RESULTS: Six themes relating to implementation influences were identified: community and staff attitudes, resource availability, organisational culture, role definition, training and support and peer support network. CONCLUSIONS: This is the first multicountry study to explore societal attitudes and organisational culture influences on the implementation of peer support. Addressing community-level discrimination and developing a recovery orientation in mental health systems can contribute to effective implementation of peer support work. The relationship between societal stigma about mental health and resource allocation decisions warrants future investigation. TRIAL REGISTRATION NUMBER: ISRCTN26008944.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".