Digital health, gender and health equity: invisible imperatives
Bibliographic record
Abstract
A growing body of evidence shows the use of digital technologies in health-referred to as eHealth, mHealth or 'digital health'-is improving and saving lives in low- and middle-income countries. Despite this prevalent and persistent narrative, very few studies examine its effects on health equity, gender and power dynamics. This journal supplement addresses these invisible imperatives by going beyond traditional measures of coverage, efficacy and cost-effectiveness associated with digital health interventions, to unpack different experiences of health workers and beneficiaries. The collection of papers presents findings from a cohort of implementation research projects in Africa, Asia, Latin America and the Middle East, and two commentaries offer observations from learning-oriented evaluative activities across the entire cohort. The story emerging from this cohort is comprised of three themes: (i) digital health can positively influence health equity; (ii) gender and power analyses are essential; and (iii) digital health can be used to strengthen upward and downward accountability. These findings, at the individual project level and at the level of the cohort, provide encouraging recommendations on how to approach the design, implementation and evaluation of digital health interventions to address the Sustainable Development Goals agenda of leaving no one behind.
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 | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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".