Building Qualitative Research Capacity Among Interdisciplinary Teams to Investigate Girls’ Challenges With Menstruation: Process and Lessons Learned From a 14-Country E-Course
Bibliographic record
Abstract
Public health–related decisions are influenced by a variety of actors operating on local to global levels, including community leaders, educators, nongovernment organizations, government officials, donors, and researchers, many of whom may lack formal public health training. The provision of public health instruction to interdisciplinary professionals has the potential to strengthen the capacity of all stakeholders to make informed, evidenced-based decisions about health policies and programs. The use of online learning is emerging as a promising means of providing public health training, particularly among those living in geographically disparate areas and from multidisciplinary backgrounds. This article describes an online course created to teach participants in stakeholder teams from 14 low- and middle-income countries how to design and conduct qualitative research to understand girls’ challenges managing menstruation at school. The goal of the course was to strengthen each country team’s ability to conduct research by building the capacity of the members. Thus, completion of the course by all team members was an objective, but less of a focus than assuring that each team as a collective was gaining public health insights and working together to make informed decisions about their research goals. This course led to benefits beyond capacity strengthening, including the formation of a broader community of learning and practice that extended beyond country boundaries. We recommend embedding training opportunities for multidisciplinary stakeholders into research endeavors given the potential for positive effects on individual participants and overall policy decisions to improve community health and provide lessons learned for doing so.
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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 | 0.137 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".