The Portrayal of Cesarean Section on Instagram: Mixed Methods Social Media Analysis
Bibliographic record
Abstract
BACKGROUND: Cesarean section (CS) rates in Indonesia are rapidly increasing for both sociocultural and medical reasons. However, there is limited understanding of the role that social media plays in influencing preferences regarding mode of birth (vaginal or CS). Social media provides a platform for users to seek and exchange information, including information on the mode of birth, which may help unpack social influences on health behavior. OBJECTIVE: This study aims to explore how CS is portrayed on Instagram in Indonesia. METHODS: We downloaded public Instagram posts from Indonesia containing CS hashtags and extracted their attributes (image, caption, hashtags, and objects and texts within images). Posts were divided into 2 periods-before COVID-19 and during COVID-19-to examine changes in CS portrayal during the pandemic. We used a mixed methods approach to analysis using text mining, descriptive statistics, and qualitative content analysis. RESULTS: A total of 9978 posts were analyzed quantitatively, and 720 (7.22%) posts were sampled and analyzed qualitatively. The use of text (527/5913, 8.91% vs 242/4065, 5.95%; P<.001) and advertisement materials (411/5913, 6.95% vs 83/4065, 2.04%; P<.001) increased during the COVID-19 pandemic compared to before the pandemic, indicating growth of information sharing on CS over time. Posts with CS hashtags primarily promoted herbal medicine for faster recovery and services for choosing auspicious childbirth dates, encouraging elective CS. Some private health facilities offered discounts on CS for special events such as Mother's Day and promoted techniques such as enhanced recovery after CS for comfortable, painless birth, and faster recovery after CS. Hashtags related to comfortable or painless birth (2358/5913, 39.88% vs 278/4065, 6.84%; P<.001), enhanced recovery after CS (124/5913, 2.1% vs 0%; P<.001), feng shui services (110/5913, 1.86% vs 56/4065, 1.38%; P=.03), names of health care providers (2974/5913, 50.3% vs 304/4065, 7.48%; P<.001), and names of hospitals (1460/5913, 24.69% vs 917/4065, 22.56%; P=.007) were more prominent during compared to before the pandemic. CONCLUSIONS: This study highlights the necessity of enforcing advertisement regulations regarding birth-related medical services in the commercial and private sectors. Enhanced health promotion efforts are crucial to ensure that women receive accurate, balanced, and appropriate information about birth options. Continuous and proactive health information dissemination from government organizations is essential to counteract biases favoring CS over vaginal birth.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".