Understanding Digital Technology Access and Use Among New York State Residents to Enhance Dissemination of Health Information
Bibliographic record
Abstract
BACKGROUND: Many state and local health departments, as well as community organizations, have been using new technologies to disseminate health information to targeted populations. Yet little data exist that show access and use patterns, as well as preferences for receiving health information, at the state level. OBJECTIVE: This study was designed to obtain information about media and technology use, and health information seeking patterns, from a sample of New York State (NYS) residents. METHODS: A cross-sectional telephone survey (with mobile phones and landlines) was developed to assess media and technology access, use patterns, and preferences for receiving health information among a sample of 1350 residents in NYS. The survey used random digit dialing methodology. A weighted analysis was conducted utilizing Stata/SE software. RESULTS: Data suggest that NYS residents have a high level of computer and Internet use; 82% have at least one working computer at home, and 85% use the Internet at least sometimes. Mobile phone use is also high; 90% indicated having a mobile phone, and of those 63% have a smartphone. When asked about preferences for receiving health information from an organization, many people preferred websites (49%); preferences for other sources varied by demographic characteristics. CONCLUSIONS: Findings suggest that the Internet and other technologies are viable ways to reach NYS residents, but agencies and organizations should still consider using traditional methods of communication in some cases, and determine appropriate channels based on the population of interest.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".