Who is More Likely to Use the Internet for Health Behavior Change? A Cross-Sectional Survey of Internet Use Among Smokers and Nonsmokers Who Are Orthopedic Trauma Patients
Bibliographic record
Abstract
BACKGROUND: eHealth presents opportunities to provide population groups with accessible health interventions, although knowledge about Internet access, peoples' interest in using the Internet for health, and users' characteristics are required prior to eHealth program development. OBJECTIVE: This study surveyed hospital patients to examine rates of Internet use, interest in using the Internet for health, and respondent characteristics related to Internet use and interest in using the Internet for health. For patients who smoke, preferences for types of smoking cessation programs for use at home and while in hospital were also examined. METHODS: An online cross-sectional survey was used to survey 819 orthopedic trauma patients (response rate: 72.61%, 819/1128) from two public hospitals in New South Wales, Australia. Logistic regressions were used to examine associations between variables. RESULTS: A total of 72.7% (574/790) of respondents had at least weekly Internet access and more than half (56.6%, 357/631) reported interest in using the Internet for health. Odds of at least weekly Internet usage were higher if the individual was born overseas (OR 2.21, 95% CI 1.27-3.82, P=.005), had a tertiary education (OR 3.75, 95% CI 2.41-5.84, P<.001), or was a nonsmoker (OR 3.75, 95% CI 2.41-5.84, P<.001). Interest in using the Internet for health increased with high school (OR 1.85, 95% CI 1.09-3.15, P=.02) or tertiary education (OR 2.48, 95% CI 1.66-3.70, P<.001), and if household incomes were more than AUS $100,000 (OR 2.5, 95% CI 1.25-4.97, P=.009). Older individuals were less interested in using the Internet for health (OR 0.98, 95% CI 0.97-0.99, P<.001). CONCLUSIONS: Online interventions may be a potential tool for health care in this hospitalized population. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12614001147673; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366829&isReview=true (Archived by WebCite at http://www.webcitation.org/6qg26u3En).
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".