Web and mobile-based technologies for monitoring high-risk pregnancies
Bibliographic record
Abstract
INTRODUCTION: High-risk pregnancy is an illness in which there are severe complications and problems that may cause fetal loss and requires continuous care. It seems that using telemedicine technology is helpful to provide wider access to prenatal care. The aim of this study was to compare the feasibility of using web-based and mobile-based technologies in caring for high-risk pregnancy. MATERIALS AND METHODS: This was a cross-sectional study and the participants included midwives and gynaecologists who worked at teaching hospitals. The data were collected by using two five-point Likert scale questionnaires which were designed based on the literature review. The questionnaires included two main sections: demographic questions and questions related to five aspects of a feasibility study. Face and content validity of the questionnaires were confirmed by the experts and the reliability was checked by using the test-retest method. The data were analysed using descriptive and inferential statistics. RESULTS: In this study, 79 questionnaires were completed by 50 midwives (63.29%) and 29 gynaecologists (36.71%). Overall, midwives (p=0.001) and gynaecologists (p=0.003) believed that using mobile-based technologies was more feasible than using web-based technologies in caring for high-risk pregnancies. CONCLUSION: It seems that planning for the future technological direction and providing mobile-based applications should be taken into account and prioritised to improve the quality of prenatal care and to increase access to healthcare services for high-risk pregnancies.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".