“What are we doing here?”: Reflections on developing a transcultural “Road Map” for global menstrual hygiene management
Bibliographic record
Abstract
Introduction: Globally, reproductive aged girls and women experience personal and social barriers to access the simplest of supplies or menstrual friendly hygiene facilities, exclusion from full participation, and even violation of their human rights, simply because they are experiencing the biological event of menstruation. In Malawi, Africa specifically, the management of menstruation is a challenge for girls and women. This paper examines the process of developing a menstrual hygiene friendly facilities tool for the Malawian context.Methods: Autobiographical narrative inquiry was used for this research. Chronological annals, personal communications, draft tool development documents, journals, text messages, photos, and mementos were used to co-construct an experiential narrative.Results: Four threads that shaped the process of this nursing research collaboration were identified through the creation of the narrative as follows: (1) feeling vulnerable, (2) our realization, (3) building collaborative relationships, and (4) revisiting the product of the research.Conclusions: Three implications for global transcultural nursing practice emerged from this research: (1) collaborative partnerships, (2) cultural adaptions of interventions, and (3) continuous learning and reflection. These implications can be used to guide future international nursing research.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.030 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".