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Record W4414021254 · doi:10.2196/75136

Use of Indigenous-Based Methodologies to Enhance the Understanding of Local Context in Ugandan Communities: Protocol for a Mixed Methods Study

2025· article· en· W4414021254 on OpenAlexaffvenue
Sahr Wali, Jeremy I. Schwartz, Justice Seidel, Jenipher Kamarembo, Jenifer Atala, Ann R. Akiteng, Martha Nabadda, Cinderella Ngonzi Muhangi, Angela Mashford‐Pringle, Heather J. Ross, Joseph A Cafazzo, Isaac Ssinabulya

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsNOSM UniversityPublic Health OntarioUniversity of TorontoTed Rogers Centre for Heart ResearchUniversity Health Network
Fundersnot available
KeywordsPreprintIndigenousProtocol (science)Context (archaeology)Adaptation (eye)Computer scienceWorld Wide WebGeographyPsychologyMedicineEcologyAlternative medicineBiology

Abstract

fetched live from OpenAlex

BACKGROUND: With many socially disadvantaged populations experiencing a higher level of illness than the general population, health research has begun to recognize the impact of social determinants on health outcomes. Community-based research has increasingly been used to understand the complexities of the local context. However, given the number of interdependent factors influencing individual well-being, no single methodology can explore this level of complexity alone. To put context into perspective, research processes need to shift from the sole use of Western methodologies and, instead, incorporate collaborative methods from nontraditional research. Specifically, Indigenous methodologies have been developed to better understand the complexity of context within multiple worldviews, but current studies have failed to apply these approaches within other cultural settings. OBJECTIVE: This mixed methods study will use Western and Indigenous methodologies to adapt a digital health program for remote communities in Uganda. METHODS: Using the principles of community-based research and user-centered design, a 4-phase mixed methods study will be conducted. The Indigenous method of 2-eyed seeing will be used to promote a reflexive engagement strategy throughout all study phases. Phase 1 will focus on partnership building to codevelop the project priorities and study design. Phase 2 will involve a needs assessment to elicit a context-focused understanding of the local clinic and community environment. Phase 3 will involve a series of system adaptations to co-design the program. Phase 4 will consist of a community-based field study to evaluate the usability and cultural relevance of the adapted program. RESULTS: This study was approved by the Makerere University School of Medicine Research and Ethics Committee (Mak-SOMREC-2021-63) and the University Health Network Research Ethics Board (20-6022). This protocol provides a novel strategy leveraging a range of community-based methods to ensure that the contextual significance of each community's challenges is reflected in the design of the Medly Uganda program. Partnership building was initiated in June 2019, and the first stage of data collection in phase 2 began in January 2021. At the time of manuscript submission, phases 1 to 3 have been completed. Phase 4 data analysis is ongoing and expected to be completed in October 2025. CONCLUSIONS: Integrating the community's local knowledge into the design of the Medly Uganda program will lead to the development of meaningful interventions that improve health outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/75136.

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)0.153
metaresearch head score (Gemma)0.134
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.153
Threshold uncertainty score0.808

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1530.134
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0080.006
Science and technology studies0.0070.006
Scholarly communication0.0070.005
Open science0.0060.005
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0490.013

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 head0.741
GPT teacher head0.709
Teacher spread0.032 · 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
GenreProtocol

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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