Discussion of Weight Loss Surgery in Instagram Posts: Successive Sampling Study
Bibliographic record
Abstract
BACKGROUND: The majority of American adults search for health and illness information on the internet. However, the quality and accuracy of this information are notoriously variable. With the advent of social media, US individuals have increasingly shared their own health and illness experiences, including those related to bariatric surgery, on social media platforms. Previous research has found that peer-to-peer requesting and giving of advice related to bariatric surgery on social media is common, that such advice is often presented in stark terms, and that the advice may not reflect patient standards of care. These previous investigations have helped to map bariatric surgery content on Facebook and YouTube. OBJECTIVE: This objective of this study was to document and compare weight loss surgery (WLS)-related content on Instagram in the months leading up to the COVID-19 pandemic and 1 year later. METHODS: We analyzed a total of 300 Instagram posts (50 posts per week for 3 consecutive weeks in late February and early March in both 2020 and 2021) uploaded using the hashtag #wls. Descriptive statistics were reported, and independent 1-tailed chi-square tests were used to determine if a post's publication year statistically affected its inclusion of a particular type of content. RESULTS: Overall, advice giving and personal responsibility for outcomes were emphasized by WLS posters on Instagram. However, social support was less emphasized. The safety, challenges, and risks associated with WLS were rarely discussed. The majority of posts did not contain references to facts from reputable medical sources. Posts published in 2021 were more likely to mention stress/hardships of living with WLS (45/150, 30%, vs 29/150, 19.3%; P=.03); however, those published in 2020 more often identified the importance of ongoing support for WLS success (35/150, 23.3%, vs 16/150, 10.7%; P=.004). CONCLUSIONS: Given that bariatric patients have low rates of postoperative follow-up, yet post-operative care and yet support are associated with improved health and weight loss outcomes, and given that health content on the web is of mixed accuracy, bariatric professionals may wish to consider including an online support forum moderated by a professional as a routine part of postoperative care. Doing so may not only improve follow-up rates but may offer providers the opportunity to counter inaccuracies encountered on social media.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".