Variations in Unmet Health Care Needs by Perceptions of Social Media Health Mis- and Disinformation, Frequency of Social Media Use, Medical Trust, and Medical Care Discrimination: Cross-Sectional Study
Bibliographic record
Abstract
Background: Unmet need for health care is defined as choosing to postpone or completely avoid necessary medical treatment despite having a need for it, which can worsen current conditions or contribute to new health problems. The emerging infodemic can be a barrier that prevents people from accessing quality health information, contributing to lower levels of seeking medical care when needed. Objective: We evaluated the association between perceptions of health mis- and disinformation on social media and unmet need for health care. In addition, we evaluated mechanisms for this relationship, including frequency of social media use, medical trust, and medical care discrimination. Methods: Data from 3964 active adult social media users responding to the 2022 Health Information National Trends Survey 6 (HINTS 6), a nationally representative survey, were analyzed. The outcome was unmet need for medical care, defined as delaying or not getting the necessary medical care. The predictor variables were perception of social media health mis- and disinformation, frequency of social media use, level of trust in the health care system, and perceived racial and ethnic discrimination when receiving health care. Results: Multivariable logistic regression models indicated that perception of substantial social media health mis- and disinformation (odds ratio [OR] 1.40, 95% CI 1.07-1.82), daily use of social media (OR 1.34, 95% CI 1.01-1.79), low medical trust (OR 1.46, 95% CI 1.06-2.01), and perceived discrimination (OR 2.24, 95% CI 1.44-3.50) were significantly associated with a higher likelihood of unmet need for medical care. Unmet need among adults who did not use social media daily and who did not perceive substantial mis- and disinformation (24%; 95% CI 19%-30%) was lower compared to daily social media users who perceived substantial mis- and disinformation (38%; 95% CI 32%-43%). Adults who perceived substantial mis- and disinformation and had low trust in health care had the highest probability of reporting unmet need (43%; 95% CI 38%-49%) compared to the other three groups. Adults who perceived substantial mis- and disinformation and experienced medical care discrimination had a statistically significant higher probability of reporting unmet need (51%; 95% CI 40%-62%) compared to adults who did not experience medical care discrimination and did not perceive substantial mis- and disinformation (29%; 95% CI 26%-32%). Conclusions: Unmet need for medical care was higher among individuals who perceived a substantial degree of social media mis- and disinformation, especially among those who used social media daily, did not trust the health care system, and experienced racial or ethnic discrimination when receiving health care. To counter the negative effects of social media mis- and disinformation on unmet need for health care, public health messaging must focus on daily social media users as well as improving trust and reducing structural racism in the health care system.
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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 | 0.004 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".