Community Health Worker Evaluation of Implementing an mHealth Application to Support Maternal Health Care in Rural India
Bibliographic record
Abstract
Introduction: PIERS on the Move (POM) is a mobile health (mHealth) application developed for a smartphone to support community health workers (CHWs) for identification and management of women at risk of adverse outcomes from pre-eclampsia. POM was implemented as an addition to routine antenatal care by accredited social health activists (ASHAs) and auxiliary nurse midwives (ANMs) during the community level intervention for pre-eclampsia (CLIP) Trial in Karnataka state, India (NCT01911494). The objective of this study was to evaluate the experiences of CHWs of using POM in rural India and their perceptions of acceptability and feasibility of this mHealth intervention. Methods: A posttrial mixed-methods evaluation was designed to measure CHW knowledge and self-efficacy regarding the care of women with pre-eclampsia and perceptions of CHWs on the ease of use and usefulness of POM. A structured survey with open-ended questions was conducted between October and November 2017. The median values on a 5-point Likert scale for knowledge and self-efficacy questions were compared between trial arms by Mann–Whitney U test ( p < 0.05 significant). Qualitative analysis was undertaken on NVivo 12 (QSR International, Melbourne, Australia). Results: A total of 48 ASHAs and ANMs were interviewed, including 24 who used POM (intervention arm) and 24 who did not (control arm). Self-reported knowledge and self-efficacy for the care of women with pre-eclampsia did not differ between groups. The qualitative analysis highlighted that health workers who used POM reported improved interactions with women and families in their communities. POM strengthened the role of ASHA as a CHW beyond a "link-worker" accompanying women to health services. With training, the mHealth application was easy to use even for CHWs who did not have much experience with smartphones. Conclusions: Community health workers found the POM app easy to use, useful, and well-received by women and their families. POM did not improve care through increased knowledge but built capacity by increasing the recognition of the ASHA and ANM as critical members of the continuum of antenatal healthcare within their communities. These findings support the important role that mHealth technologies can play in strengthening health systems to reach rural, remote, and marginalized populations to reduce disparities in health.
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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 | 0.016 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".