The Role of mHealth Interventions in Changing Gender Relations: Systematic Review of Qualitative Findings
Bibliographic record
Abstract
BACKGROUND: The rapid and widespread growth of mobile technologies in low- and middle-income countries can offer groundbreaking ways of disseminating public health interventions. However, gender-based inequalities present a challenge for women in accessing mobile technology. Research has shown that mobile health (mHealth) interventions can affect gender relations in both positive and negative ways; however, few mHealth programs use a gender-sensitive lens when designing, implementing, or analyzing programs. OBJECTIVE: This systematic review aims to identify and summarize the findings of qualitative research studies that explore the impact of mHealth interventions on gender relations as a result of participating in such initiatives in low- and middle-income countries. METHODS: We performed a systematic literature review to examine empirical evidence of changes in gender relations attributed to participation in an mHealth intervention in low- and middle-income countries. Peer-reviewed articles were included based on whether they evaluated an mHealth intervention and were published between 2013 and 2020. Articles using mHealth that solely targeted health workers, did not assess a specific intervention, used mobile technology for data collection only, or were formative or exploratory in nature were excluded. The search terms were entered into 4 key electronic databases-MEDLINE, EMBASE, PsycINFO, and Scopus-generating a comprehensive list of potentially relevant peer-reviewed articles. Thematic analysis was used to identify, analyze, and report the themes that emerged from our data. RESULTS: Of the 578 full-text articles retrieved, 14 (2.4%) were eligible for inclusion in the study. None of the articles appraised gender from the outset. The articles uncovered findings on gender relations through the course of the intervention or postprogram evaluation. Most studies took place in sub-Saharan Africa, with the remainder in South and Southeast Asia. The articles focused on maternal and child health, HIV diagnosis and treatment, and reproductive health. This review found that mHealth programs could enhance spousal communication, foster emotional support between couples, improve women's self-efficacy and autonomy in seeking health information and services, and increase their involvement in health-related decision-making. Despite the positive impacts, some mHealth interventions had an adverse effect, reinforcing the digital divide, upholding men as gatekeepers of information and sole decision-makers, and exacerbating relationship problems. CONCLUSIONS: These results suggest that given the rapid and persistent upscale of mHealth interventions in low- and middle-income settings, it is imperative to design interventions that consider their impact on power dynamics and gender relations. Future research is needed to fill the evidence gaps on gender and mHealth, acknowledging that women are not passive beneficiaries and that they need to actively participate and be empowered by mHealth interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".