Social Media Use among American Indian and Alaska Native People: Implications for Health Communication Strategies
Bibliographic record
Abstract
Patients, health professionals, and communities use social media to communicate information about health determinants and associated risk factors. Studies have highlighted the potential for social media to reach underserved populations, suggesting these platforms can be used to disseminate health information tailored for diverse and hard-to-reach populations. Little is known, however, about the use of social media among American Indian and Alaska Native populations. The objective of this cross-sectional study is to better understand the use of social media platforms to disseminate information across these populations. Our team surveyed 429 American Indian and Alaska Native adults attending cultural events in Washington State on their use of various types of social media. We used logistic regressions to assess participant use of Twitter, Snapchat, Facebook, and Instagram as related to participant demographics, including age, gender, education, and their place of residence (on-reservation, rural off-reservation areas, or large metropolitan areas). Findings showed that Facebook was used by more participants than other platforms (79%), followed by Instagram (31%). Nearly half of participants used only one social media platform (48%). Age was negatively associated with using Instagram (0.8 OR, 95% CI: 0.7, 0.9) and Snapchat (0.6 OR, 95% CI: 0.5, 0.7). College education was associated with higher odds of using an additional social media platform compared to those without any college education (2.0 OR, 95% CI: 1.1, 3.6). Most participants used social media platforms, which suggests these platforms may be a useful tool in disseminating information to American Indian and Alaska Native peoples. Further research should document how social media can be used to effectively disseminate risk and health information and assess whether it can influence health knowledge and behaviors among these populations.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".