Perceived barriers and facilitators of implementing a sustained smartphone-based telemonitoring program for pregnant women at high-risk for pre-eclampsia in the public and private sectors in Pakistan
Bibliographic record
Abstract
Background: In Pakistan, a smartphone-based telemonitoring (TM) program (Raabta) has been designed to support pregnant women with high risk for preeclampsia (HRPE) in Pakistan. However, implementing TM interventions is often challenging, particularly in low-resource settings, given the complexity of healthcare environments and variations in public and private health sectors. This study explores the potential barriers and facilitators for a sustained implementation of the Raabta program in public and private sector hospitals in Pakistan. Methods: 6). Participants were recruited using purposive and snowball sampling techniques. Interview transcripts were deductively analyzed using the Consolidated Framework for Implementation Research (CFIR) domains. Results: Based on the CFIR domains, the findings included: (1) Raabta being perceived as user-friendly even for patients with low digital and language literacy; (2) Outer settings: Limited health and digital literacy, poor language proficiency, and cultural norms can influence the willingness and ability of public sector patients to use the Raabta; (3) Inner settings: The private health sector is well-equipped for the Raabta implementation, while the public health sector faces challenges related to physical space, limited human and financial resources, and physician resistance; (4) Individual characteristics: Majority participants demonstrated positive attitudes toward the Raabta program and expressed confidence in using it (5) Process: Recommendations included adopting a nurse-led model for the private sector, leveraging paramedics for monitoring the Raabta dashboard, integrating Raabta with existing digital platforms, and establishing an advisory committee for program sustainability. Conclusion: Raabta implementation may be more feasible in the private sector due to patient demographics, health and digital literacy, cultural norms, financial resources, physician readiness, and hospital infrastructure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".