Health Advice from Instagram Influencers on Polycystic Ovary Syndrome (<scp>PCOS</scp>): Their Strategies to Establish and Manipulate Credibility
Bibliographic record
Abstract
ABSTRACT Individuals with polycystic ovary syndrome (PCOS) turn to Instagram influencers as an alternative or complementary PCOS information source to their physicians. However, the ambiguous qualifications of influencers, and contradicting claims regarding how to manage PCOS, make it difficult to identify credible sources. Using a directed qualitative content analysis, we examined the range of strategies that 10 Instagram PCOS influencers used to signal credibility. We found that influencers utilized a range of pseudo‐credibility strategies. Another key contribution of the study is a description of how PCOS influencers reference sources to signal credibility. We also found that Instagram PCOS influencers discuss what it means to be healthy with PCOS, and we describe how they contribute to a wider cultural participation in healthism. The findings build upon the source credibility literature to identify novel credibility cues used by influencers as social media platforms evolve, and further, reveal the underlying healthism‐centric discourse of PCOS influencers.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".