Integrating dementia care into primary health services: lay health workers with internal facilitation in Uganda
Bibliographic record
Abstract
OBJECTIVES: Dementia care is underdeveloped in low- and middle-income countries (LMICs), with limited specialist services. In Uganda's decentralized health system, Health Assistants supervise Lay Health Workers (LHWs), yet dementia care is not part of their remit. This study explored the feasibility of the WHO Lay Health Worker Dementia Care model with Internal Facilitation (WLDC+IF), in which Health Assistants support LHWs in delivering community-based dementia care. METHOD: We conducted formative qualitative in-depth interviews with eight LHWs from two rural parishes in northern Uganda. Guided by the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, thematic analysis examined three domains: implementation support, process improvement, and practice sustainment. The WHO Dementia Toolkit and the facilitator role were introduced conceptually during interviews to assess perceived feasibility. RESULTS: LHWs were willing to deliver dementia care but cited limited training, lack of job aids, and unclear referral pathways as barriers. Health Assistants were viewed as trusted supervisors who could offer structured guidance and feedback. Participants emphasized the importance of public sensitization to reduce stigma, caregiver support groups to address isolation and burden, job-aid materials for education, and environmental modifications (home safety). Follow-up visits, documentation, and feedback were identified as practical ways to sustain practice. CONCLUSION: WLDC+IF is a feasible, leadership-centered strategy that leverages existing health system structures to integrate dementia care into primary services. Positioning Health Assistants as internal facilitators may strengthen local supervision, build LHW capacity, and address psychosocial/clinical needs, with potential to reduce caregiver burden, and improve quality of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".