Experiences With a Postpartum mHealth Intervention During the COVID-19 Pandemic: Key Informant Interviews Among Patients, Health Care Providers, and Stakeholders
Bibliographic record
Abstract
BACKGROUND: Maternal morbidity and mortality in the United States continue to be a worsening public health crisis, with persistent racial disparities among Black women during the COVID-19 pandemic. Innovations in mobile health (mHealth) technology are being developed as a strategy to connect birthing women to their health care providers during the first 6 weeks of the postpartum period. OBJECTIVE: This study aimed to inform a process to evaluate the barriers to mHealth implementation in the context of the COVID-19 pandemic by exploring the experiences of mothers and stakeholders who were directly involved in the pilot program. METHODS: The qualitative design used GoToMeeting (GoTo) individual interviews of 13 mothers and 7 stakeholders at a suburban teaching hospital in New Jersey. Mothers were aged ≥18 years, able to read and write in English or Spanish, had a vaginal or cesarean birth at >20 weeks of estimated gestational age, and were admitted for delivery at the hospital with at least a 24-hour postpartum stay. Stakeholders were part of the hospital network's obstetrics collaborative subcommittee comprising administrators, physicians, registered nurses, and informatics. Responses were transcribed verbatim and analyzed for emerging themes. The socioecological framework provided a holistic lens for analyzing the multilevel influences on individual experiences. RESULTS: A total of 3 major themes were identified: mothers experienced barriers from personal situations at home and with services in the hospital and community, which were intensified by the COVID-19 pandemic; the COVID-19 pandemic negatively impacted hospital services, priorities, and individual staff; and mothers and stakeholders had positive experiences and perceptions of the mHealth intervention. CONCLUSIONS: The use and reach of the mHealth intervention were negatively influenced by interrelated factors operating at multiple levels. The system-wide and multilevel impact of the pandemic was reflected in participants' responses, providing evidence for the need to re-evaluate mHealth implementation with more adaptable systems and structures in place using a socioecological framework.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".