Design of the Maternal Website EMAeHealth That Supports Decision-Making During Pregnancy and in the Postpartum Period: Collaborative Action Research Study
Bibliographic record
Abstract
BACKGROUND: Despite the benefit maternal education has for women, it needs new tools to increase its effectiveness and scope, in tune with the needs of current users. OBJECTIVE: We attempted to develop a multifunctional personalized eHealth platform aimed at the self-management of health in relation to maternity, which can be considered a flexible and adaptable maternal education tool. METHODS: The International Patient Decision Aid Standards (IPDAS) were applied. A website prototype was developed for implementation in the public health system using a collaborative action research process, in which experts and patients participate, with qualitative research techniques, as well as focus groups, prioritization, and consensus techniques. RESULTS: We have proposed a website that includes (1) systematically updated information related to clinical practice guidelines, (2) interaction between peers and users/professionals, (3) instruments for self-assessment of health needs as a basis for working on counseling, agreement on actions, help in the search for resources, support in decision-making, and monitoring and evaluation of results, and (4) access for women to their clinical data and the option of sharing the data with other health agents. These components, with different access requirements, would be reviewed through iterative cycles depending on the frequency and effectiveness resulting from their use and would be accessible from any digital device. CONCLUSIONS: A website that supports maternal education should contain not only information, but also resources for individual attention and social support. Its usefulness for the health and satisfaction of women should be evaluated in various different environments.
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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 | 0.050 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".