Enjeux éthiques du recours à Internet par les femmes enceintes dans leur suivi de grossesse
Bibliographic record
Abstract
INTRODUCTION: Pregnant women are heavy users of Internet and this has an impact on their medical follow-up. The purpose of this study is to highlight the ethical issues related to the use of the Internet by women in their medical care.Methode: Through a systematic literature review conducted on PubMed/Medline, Web of Science, CINAHL and Embase between June and July 2019, 10 670 results were obtained, and 79 articles were included in the post-selection study. A thematic analysis was conducted on these articles. RESULTS: More than 90% of pregnant women use Internet, particularly to find medical information and social support, mainly on pregnancy and childbirth. This research allows them more equitable access to knowledge and develops their empowerment, which modifies the relationship between caregiver and patient, through the acquisition of greater autonomy for women and the development of experiential knowledge. This access offers a central and active role to pregnant women in their medical care. However, many authors also agree on the possible abuses of this use: misinformation, disproportionate information and the presence of judgment that undermine empowerment, but also digital divide and inequity in understanding information, stigmatization of women, and risks of privacy breaches on data acquired online. CONCLUSION: In order to provide pregnant women with the central and active place they seek, the authors recommend involving caregivers in the referral to reliable sites, encouraging them to develop online content, and educating pregnant women in the search for health information on Internet.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| 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".