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Record W7057228000

Internal Facilitation by Health Assistants for the “WHO Lay Health Worker Dementia Care” in Rural Uganda: A Formative Evaluation

2025· article· en· W7057228000 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaFormative assessmentHealth careQualitative researchCall to actionAction researchRural healthQualitative property
DOInot available

Abstract

fetched live from OpenAlex

Edith K Wakida,1,2 Celestino Obua,2 Godfrey Zari Rukundo,3 Mary Samantha,2 Samuel Maling,4 Christine K Karungi,2 Zohray M Talib,1 Jessica Haberer,5,6 Stephen J Bartels5,6 1Department of Medical Education, California University of Science and Medicine, Colton, California, USA; 2Department of Research and Development, Alpha Center for Research Administration, Mbarara, Uganda; 3Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, Ontario, Canada; 4Department of Psychiatry, Mbarara University of Science and Technology, Mbarara, Uganda; 5Department of Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; 6Harvard Medical School, Harvard University, Boston, Massachusetts, USACorrespondence: Edith K Wakida, Email Edith.Wakida@cusm.eduBackground: Dementia is characterized by cognitive symptoms like memory loss, difficulty with language, and impaired judgment, alongside behavioral and psychological symptoms such as depression, anxiety, and aggression. Early diagnosis and tailored care are essential for managing these symptoms, improving quality of life, and reducing caregiver burden. Dementia affects a substantial portion of older people globally, especially in low- and middle-income countries like Uganda, where rural healthcare systems face challenges in dementia care access. To address these needs, we gathered key stakeholders’ perspectives on a culturally tailored model employing lay health workers, supported by health assistants as internal facilitators, to implement the World Health Organization dementia toolkit in rural communities.Methods: We conducted a formative qualitative study, utilizing one-on-one interviews with health assistants, district health team members, and primary healthcare providers in rural Uganda. We solicited their perspectives on implementing the World Health Organization dementia toolkit at the village level. The integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework guided data collection and analysis, focusing on implementation support, process improvement, and practice sustainment.Results: Strong support was found for health assistants’ roles in facilitating lay health worker-led dementia care at the community level. Health assistants’ familiarity with lay health workers and pre-established structures were considered facilitating factors. Key challenges included knowledge gaps in dementia care and limited resources. Participants emphasized the importance of training, mentorship, and standardized reporting tools to enhance the implementation of dementia care. They recommended providing the health assistants with job guides, updated reporting templates to collect dementia indicators, and orientation on what they should do during internal facilitation with the lay health workers using the dementia toolkit.Conclusion: Health assistants’ internal facilitation provides a promising strategy for scaling dementia care in rural Uganda by leveraging community-based lay health workers. Addressing identified knowledge gaps, communication needs, and resource constraints will be essential to sustaining dementia care interventions in these communities.Keywords: dementia care, internal facilitation, lay health workers, Uganda, rural health, i-PARIHS framework

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.078
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.412

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0780.110
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0030.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.155
GPT teacher head0.577
Teacher spread0.422 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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