A Digital Toolkit (M-Healer) to Improve Care and Reduce Human Rights Abuses Against People With Mental Illness in West Africa: User-Centered Design, Development, and Usability Study
Bibliographic record
Abstract
BACKGROUND: The resources of West African mental health care systems are severely constrained, which contributes to significant unmet mental health needs. Consequently, people with psychiatric conditions often receive care from traditional and faith healers. Healers may use practices that constitute human rights violations, such as flogging, caging, forced fasting, and chaining. OBJECTIVE: The aim of this study is to partner with healers in Ghana to develop a smartphone toolkit designed to support the dissemination of evidence-based psychosocial interventions and the strengthening of human rights awareness in the healer community. METHODS: We conducted on-site observations and qualitative interviews with healers, a group co-design session, content development and prototype system build-out, and usability testing. RESULTS: A total of 18 healers completed individual interviews. Participants reported on their understanding of the causes and treatments of mental illnesses. They identified situations in which they elect to use mechanical restraints and other coercive practices. Participants described an openness to using a smartphone-based app to help introduce them to alternative practices. A total of 12 healers participated in the co-design session. Of the 12 participants, 8 (67%) reported having a smartphone. Participants reported that they preferred spiritual guidance but that it was acceptable that M-Healer would provide mostly nonspiritual content. They provided suggestions for who should be depicted as the toolkit protagonist and ranked their preferred content delivery modality in the following order: live-action video, animated video, comic strip, and still images with text. Participants viewed mood board prototypes and rated their preferred visual design in the following order: religious theme, nature motif, community or medical, and Ghanaian culture. The content was organized into modules, including an introduction to the system, brief mental health interventions, verbal de-escalation strategies, guided relaxation techniques, and human rights training. Each module contained several scripted digital animation videos, with audio narration in English or Twi. The module menu was represented by touchscreen icons and a single word or phrase to maximize accessibility to users with limited literacy. In total, 12 participants completed the M-Healer usability testing. Participants commented that they liked the look and functionality of the app and understood the content. The participants reported that the information and displays were clear. They successfully navigated the app but identified several areas where usability could be enhanced. Posttesting usability measures indicated that participants found M-Healer to be feasible, acceptable, and usable. CONCLUSIONS: This study is the first to develop a digital mental health toolkit for healers in West Africa. Engaging healers in user-centered development produced an accessible and acceptable resource. Future field testing will determine whether M-Healer can improve healer practices and reduce human rights abuses.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".