Participation in a mobile health intervention trial to improve retention in HIV care: does gender matter?
Bibliographic record
Abstract
Background To be consistent with the United Nations' sustainable development goals on gender equality, mobile health (mHealth) programmes should aim to use communications technology to promote the empowerment of women. We conducted a pre-trial analysis of data from the WelTel Retain study on retention in HIV care to assess gender-based differences in phone access, phone sharing and concerns about receiving text messages from a healthcare provider. Methods Between April 2013-June 2015, HIV-positive adults were screened for trial participation at two clinics in urban slums in Nairobi, Kenya. Proportions of men and women excluded from the trial due to phone-related criteria were compared using a chi-square test. Gender-based differences in phone sharing patterns and concerns among trial participants were similarly compared. Results Of 1068 individuals screened, there was no difference in the proportion of men ( n = 39/378, 10.3%) and women ( n = 71/690, 10.3%) excluded because of phone-related criteria ( p-value = 0.989). Among those who shared their phone, women ( n = 52/108, 48.1%) were more likely than men ( n = 6/60, 10.0%) to share with other non-household and household members ( p < 0.001). Few participants had concerns about receiving text messages from their healthcare provider; those with concerns were all women ( n = 6/700). Discussion In this study, men and women were equally able to participate in a trial of an mHealth intervention. Equitable access in these urban slums may indicate the 'gender digital divide' is narrowing in some settings; however, gender-specific phone sharing patterns and concerns regarding privacy must be fully considered in the development and scale-up of mHealth programmes.
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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 | 0.031 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".