Prioritising gender, equity, and human rights in a GRADE-based framework to inform future research on self care for sexual and reproductive health and rights
Bibliographic record
Abstract
Introduction: In January 2019, the WHO reviewed evidence to develop global recommendations on self-care interventions for sexual and reproductive health and rights (SRHR). Identification of research gaps is part of the WHO guidelines development process, but reliable methods to do so are currently lacking with gender, equity and human rights (GER) infrequently prioritised. Methods: We expanded a prior framework based on Grading of Evidence, Assessment, Development and Evaluation (GRADE) to include GER. The revised framework is applied systematically during the formulation of research questions and comprises: (1) assessment of the GRADE strength and quality rating of recommendations; (2) mandatory inclusion of research questions identified from a global stakeholder survey; and (3) selection of the GER standards and principles most relevant to the question through discussion and consensus. For each question, we articulated: (1) the most appropriate and robust study design; (2) an alternative pragmatic design if the ideal design was not feasible; and (3) the methodological challenges facing researchers through identifying potential biases. Results: We identified 39 research questions, 7 overarching research approaches and 13 discrete feasible study designs. Availability and accessibility were most frequently identified as the GER standards and principles to consider when planning studies, followed by privacy and confidentiality. Selection and detection bias were the primary methodological challenges across mixed methods, quantitative and qualitative studies. A lack of generalisability potentially limits the use of study results with non-participation in research potentially highest in more vulnerable populations. Conclusion: A framework based on GRADE that includes stakeholders' values and identification of core GER standards and principles provides a practical, systematic approach to identifying research questions from a WHO guideline. Clear guidance for future studies will contribute to an anticipated 'living guidelines' approach within WHO. Foregrounding GER as a separate component of the framework is innovative but further elaboration to operationalise appropriate indicators for SRHR self-care interventions is required.
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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 | 0.268 | 0.370 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.024 | 0.010 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.008 | 0.011 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".