Unraveling Online Perspectives and Misinformation Surrounding Urinary Tract Infections: A Thematic Analysis of 1200 Instagram Posts
Bibliographic record
Abstract
Objective: Urinary tract infections are burdensome for patients. Social media is increasingly used as a platform for patients and public to seek and share support. The study aimed to evaluate what patients encounter when they turn to Instagram for urinary tract infection-related support and advice. Methods: The first 200 posts appearing on the "top posts" section of Instagram for 6 key hashtags (#UTI, #UTIs, #urinarytractinfection, #urinarytractinfections, #bladderin- fection, #bladderinfections) were selected. Thematic analysis (TA) was used to identify themes present in the Instagram captions. Results: Across 1200 posts analyzed, 5 main themes were identified. 1) "We can help…," this was largely commercial advertising with the promotion of healthcare clinics. 2) "I'm suffering," which contained first-person narratives about an unpleasant experience with a disease or treatment as well as frustration at health services. 3)!"Warning signs," posts describing signs or symptoms that the creator claims indicate poor health. 4)!"Remedies," these posts detailed therapies to try, often herbal. 5) "Avoid and change," which covered triggers to avoid symptom flare ups. Conclusion: There is a large amount of content on social media related to UTIs. Urologists should be aware that patients may have sought out advice using these platforms and may therefore have received misinformation and products that have been advertised but lack scientific evidence.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".