Patient Influencers: Understanding Cultural Inclusivity in Health Communication on Social Media
Bibliographic record
Abstract
A new form of social media influencer is the patient influencer, or patients who share “lived experiences” of chronic disease online and who build communities of patients. Trust in the healthcare system is a challenge for people of color due to the systemic racism and other barriers encountered. This article explores the intersection of health communication, patient influencers, and cultural inclusivity. Using the Theory of Planned Behavior’s theoretical constructs (subjective norms, personal attitudes, and perceived behavioral control), thematic analysis was used to understand culturally inclusive health communication strategies used by patient influencers of color. In-depth interviews ( N = 18) were conducted. Findings suggest that patient influencers of color have the capacity to promote inclusivity and trust within their social networking communities. Patient influencers want to empower others through their authentic content about living with and managing chronic disease. Theoretical and practical applications are addressed.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".