Problematising menstrual tracking apps: presenting a novel critical scoping review methodology for mapping and interpreting research literature
Bibliographic record
Abstract
OBJECTIVES: To showcase a novel, theoretically informed methodology for conducting scoping reviews by operationalising critical theory. And to advance the field of women's digital health by applying this critical scoping review methodology (CSR) to research on menstrual tracking apps (MTAs). METHODS AND MEASURES: 116 articles published in English, between November 2015 and November 2023, focusing on MTAs, and/or user's experiences of MTAs, were thematically analysed through the Foucauldian concept of problematisation and analytics from critical psychology. This method examined what was produced as a problem, and the underpinning discourses, subject positions, paradigms, desired outcomes, and absences within these problem categories. RESULTS: Four problematisations were identified, (1) the problem of data privacy (subproblems: type of data, consent, abortion surveillance); (2) the problem with efficacy (subproblems: evaluating efficacy, accuracy, useability); (3) the problem of regulation (subproblems: self-surveillance, normative femininity, hormonal imperative, cycle regularity imperative, menstrual stigma); and (4) the problem of women (subproblems: health literacy, technology use, medically unknown, hard to design for). CONCLUSION: MTA researchers would benefit from understanding their field through these problematisations. The CSR offers an important theoretically informed methodology for mapping and interpreting a research literature, which can identify, and expand, possibilities for research thought and practice.
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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 | 0.314 | 0.361 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.026 | 0.017 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".