The Influence of Social Media on Intrapartum Decision Making
Bibliographic record
Abstract
Social media has been influential in decision making regarding a number of health concerns. However, comparatively little has been examined with regard to its effects on pregnant women. The goal of this scoping review was to examine the literature and identify the role of social media in intrapartum decision making. A scoping review of the literature published between January 1990 and June 2018 was performed using PubMed, CINAHL, EMBASE, PsychINFO, Web of Science, and Cochrane databases. Of the initial 1951 records reviewed, 5 met inclusion criteria. Two of the 5 were quantitative in design, 1 was qualitative, and 2 used mixed methods. Internationally widespread, studies largely took place in developed nations including the United States, the United Kingdom, Canada, Australia, New Zealand, and Finland. Women are using the Internet, including social media, consistently as a source of pregnancy information, for example, 97% of 2400 participates in 1 exploratory study. This knowledge seeking was found to increase women's confidence and self-assurance in making decision during labor and birth. Studies identified issues surrounding women's ability to appraise available information. While it is clear that social media has an influence on women's intrapartum decision making, it is not clear exactly how. Further studies are needed to determine the content of the social media being appraised, the accuracy of the information, and the resulting decision as it affects the intrapartum experience. In addition, efforts should be made to open lines of communication between patients and care providers. This may foster a greater clinical understanding of social media consumption and its influences.
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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 | 0.020 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".