Education Matters: Certified health professionals have higher credibility than non health professionals on Instagram
Bibliographic record
Abstract
Social media serves as an accessible source of health information and nutrition information. Instagram, an internationally known social media platform with an average of more than 1 billion monthly active users, allows its users to create and share content. However, the credibility of the nutrition content created by users with unknown qualifications may be questionable. The objective of this study is to assess the credibility of content created by nutrition influencers on Instagram by comparing health professionals with non-health professionals. For this study, “influencer” is defined as an Instagram user with at least 15,000 followers who promotes products, services, or ideas and who creates nutrition- or health-related content. For each influencer (n=29), two posts were selected every month from August 2018 to July 2019. Using the “Credible Information Factsheet” from the Dietitians of Canada, a credibility score based on four dichotomous criteria was created. Looking at the 24 posts of each influencer holistically, a credibility score out of 4 was calculated, with 0 being the least credible and 4 being the most credible. Without exception, a greater proportion of health professionals compared to non-health professionals met each criterion from the “Credible Information Factsheet”. 92% of the health professionals met criteria 1 (Miracle Cure) compared to only 31% of non-health professionals. This demonstrates how the vast majority of health professionals would not promise a miracle cure, while most non-health professionals would readily promise a miracle cure. Additionally, 46% of health professionals met criteria 4 (Research-based) compared to only 19% of non-health professionals, which demonstrates how non-health professionals do not support claims with research. When looking at the total credibility scores for health professionals and non-health professionals, not a single health professional scored a total of 0, while not a single non-health professional scored a total of 4. Most importantly, health professionals had an average credibility score of 2.4, which is twice as high as that of non-health professionals (1.2). Overall, health professionals appeared to be more credible than non-health professionals. By viewing nutrition information posted on Instagram by non-health professionals, followers potentially expose themselves to misinformation. Further research should be undertaken to validate the credibility score based on the “Credible Information Factsheet” by determining how adept the factsheet is at differentiating credibility for Instagram content.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".