Codevelopment of a Shared Decision-Making Resource Platform for Women and Girls in 3 Low- and Middle-Income Countries (ENGAGEEs): Protocol for a Participatory Research Project
Bibliographic record
Abstract
BACKGROUND: Women and girls around the world face significant barriers in participating in health care decisions, particularly in low- and middle-income countries. Despite growing interest in shared decision-making (SDM), little is known about its implementation in these countries, and no rigorous assessment of the decision-making needs of women and girls in these contexts has been conducted. There is little SDM training and there are few decision-support tools (DSTs) specifically designed for health care decision-making by women and girls in these countries. DSTs (paper or digital) present options and probabilities and help users articulate their values and preferences. OBJECTIVE: We aim to codevelop an SDM resource platform, including locally relevant DSTs and instructional materials, to support SDM with women and girls in Brazil, Cameroon, and Senegal. METHODS: We will conduct a 4-phase participatory research project. We will use the Gender-Based Analysis Plus tool to support inclusive participatory research throughout the study. First, we will follow the Integrated Knowledge Translation framework to form a local steering committee in each country, including patients and community representatives, health and social service professionals, and decision-makers. Second, we will follow the Ottawa Decision Support Framework to conduct individual interviews and focus groups to identify the decision-making needs of women and girls. We will include 20 women and girls, along with their family members, 15 health and social service professionals, and 15 representatives of community-based organizations in each country (n=150). The Double Diamond human-centered design framework will be used to codevelop a digital SDM resource platform where DSTs and instructional materials for SDM training can be made accessible. Finally, we will assess the scalability of the platform by using the Innovation Scalability Self-Administered Questionnaire. We will report our study by using the Standards for Reporting Qualitative Research guideline and the Guidance for Reporting Involvement of Patients and the Public. RESULTS: Financial support for this project was received on February 1, 2023. This protocol was submitted during data collection but before analysis. Data collection began in January 2025. By July 2025, 92 of the 150 participants had been recruited. We expect to publish our results in December 2026. CONCLUSIONS: Ultimately, more women and girls from low- and middle-income countries will be involved in health care decisions, and more clinical teams will be able to integrate SDM into their care practices. The local steering committees will ensure equitable partnerships and the SDM resource platform will promote more inclusive approaches to SDM worldwide. The scalability assessment will help us plan to expand the impact of the platform to other regions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/78618.
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 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 | 0.077 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".