Formative evaluation to assess communication technology access and health communication preferences of Alaska Native people
Bibliographic record
Abstract
Objective:Information technology can improve the quality, safety, and efficiency of healthcare delivery by improving provider and patient access to health information. We conducted a nonrandomized, cross-sectional, self-report survey to determine whether Alaska Native and American Indian (AN/AI) people have access to the health communication technologies available through a patient-centered medical home. Methods: In 2011, we administered a self-report survey in an urban, tribally owned and operated primary care center serving AN/AI adults. Patients in the center’s waiting rooms completed the survey on paper; center staff completed it electronically. Results: Approximately 98% (n = 654) of respondents reported computer access, 97% (n = 650) email access, and 94% (n = 631) mobile phone use. Among mobile phone users, 60% had Internet access through their phones. Rates of computer access (p = .011) and email use (p = .005) were higher among women than men, but we found no significant gender difference in mobile phone access to the Internet or text messaging. Respondents in the oldest age category (65–80 years of age) were significantly less likely to anticipate using the Internet to schedule appointments, refill medications, or communicate with their health care providers (all p < .001). Conclusion:Information on use of health communication technologies enables administrators to deploy these technologies more efficiently to address health concerns in AN/AI communities. Our results will drive future research on health communication for chronic disease screening and health management.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".