Preuse Acceptance of a Family-Centered, Need-Based, and Interprofessional Perinatal Care Mobile Health Intervention: Exploratory Study
Bibliographic record
Abstract
Background: The perinatal period is one of the most vulnerable times a woman experiences. Multidimensional, interprofessional, and personalized support is needed to improve outcomes in women's and children's health while strengthening partner relationships at the same time. Although a vast amount of support services already exist in Germany for psychosocial counseling during the perinatal period, groups who are especially at risk do not take advantage of them. Objective: Family eNav is an app-based intervention developed by experts in the field of medical and psychosocial support to help young parents navigate through primary and secondary care services in Germany according to their needs. It also empowers patient and parenting perspectives through self-education and symptom monitoring for different settings, for example, mental health and preterm birth. While the intervention will be evaluated in a multicenter, randomized, controlled trial, the focus here lies on the conception of the app, demand among patients, and preuse acceptance. Methods: During the conception phase, we conducted an explorative study with prospective users and experts in the perinatal psychosocial field to understand the need and preuse acceptance of the intervention. We interviewed 20 participants with a semistructured guide, analyzing their responses using systematic text condensation. Additionally, we conducted a short survey on general questions concerning digitalization within the health care system among the participants. Results: We established two main themes: (1) access and barriers to health care and psychosocial services and (2) high preuse acceptance of app-based intervention. Health care and psychosocial providers indicated that there is a high demand for their services, which cannot always be met immediately, and at the same time, they are doubtful of reaching those individuals most in need. Prospective users and health and social care providers alike showed great interest in the perinatal navigator and suggested a variety of needs and content requirements to be included. Regionality, availability, and individualized content were underlined as success factors for high user acceptance. Barriers consisted of data protection concerns, as well as denial of their own needs. Conclusions: Our findings show great acceptance for an app-based intervention on the part of both prospective users and service providers. Feedback on requirements and content, as well as possible barriers, was taken into consideration while developing the app.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".