Barriers and Facilitators for the Use of a Medical Mobile App to Prevent Work-Related Risks in Pregnancy: A Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: The number of women participating in the labor market in Europe has increased over the last several decades. At the same time, there is growing evidence that certain conditions of employment during pregnancy may have a negative influence on pregnancy outcomes. In order to better inform pregnant women, we aim to develop an app to help assess the health risk as a result of personal and work-related factors and provide personal advice for these women and their health care providers. OBJECTIVE: The aim of this study was to compose a thematic overview of the perceived facilitators and barriers according to pregnant women, medical professionals, and employers for the use of a mobile app in obstetrical care to prevent occupational-related pregnancy complications. METHODS: Two multidisciplinary focus group meetings with in total 14 participants were conducted with pregnant women, occupational physicians, general practitioners, midwives, obstetricians, and representatives of trade unions and employer organizations. Transcripts were analyzed by qualitatively coding procedures and constant comparative methods. RESULTS: We identified 24 potential facilitators and 12 potential barriers for the use of the app in 4 categories: content of the app, the app as a mean to provide information, ease of use, and external factors. The 3 main facilitators identified were the need for a good interaction between the app and the user, apps were viewed as a more practical source of information, and the information should be understandable, according to the existing guidelines, and well-dosed. The 2 main barriers for use were extensive battery and memory use of the smartphone and sending frequent push notifications. CONCLUSIONS: The results of this study are important considerations in the developing process of a medical app implementing a guideline or evidence-based information in practice.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".