Content analysis of promotional material for asthma-related products and therapies on Instagram
Bibliographic record
Abstract
BACKGROUND: Increasingly, social media is a source for information about health and disease self-management. We conducted a content analysis of promotional asthma-related posts on Instagram to understand whether promoted products and services are consistent with the recommendations found in the Global Initiative for Asthma (GINA) 2019 guidelines. METHODS: We collected every Instagram post incorporating a common, asthma-related hashtag between September 29, 2019 and October 5, 2019. Of these 2936 collected posts, we analyzed a random sample of 266, of which, 211 met our inclusion criteria. Using an inductive, qualitative approach, we categorized the promotional posts and compared each post's content with the recommendations contained in the 2019 GINA guidelines. Posts were categorized as "consistent with GINA" if the content was supported by the GINA guidelines. Posts that promoted content that was not recommended by or was unrelated to the guidelines were categorized as "not supported by GINA". RESULTS: Of 211 posts, 89 (42.2%) were promotional in nature. Of these, a total of 29 (32.6%) were categorized as being consistent with GINA guidelines. The majority of posts were not supported by the guidelines. Forty-one (46.1%) posts promoted content that was not recommended by the current guidelines. Nineteen (21.3%) posts promoted content that was unrelated to the guidelines. The majority of unsupported content promoted non-pharmacological therapies (n = 39, 65%) to manage asthma, such as black seed oil, salt-room therapy, or cupping. CONCLUSIONS: The majority of Instagram posts in our sample promoted products or services that were not supported by GINA guidelines. These findings suggest a need for providers to discuss online health information with patients and highlight an opportunity for providers and social media companies to promote evidence-based asthma treatments and self-management advice online.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".