The future state of patient engagement? Personal health information use, attitudes towards health, and health behavior
Bibliographic record
Abstract
The past decade has given rise to interest in the effects of health care information on personal well-being. However, investigations to-date typically centered on mass communication programs in health rather than on individuals’ psychosocial characteristics, agency, and behaviors associated with their engagement with this information. Considering this gap, we examined whether the availability of personal health information is associated with health prevention behaviors in the United States. Using multivariable path analysis and data from the 2017 Health Information National Trends Survey, we investigate whether the use of personal health information is associated with positive, preventative health behaviors (healthy eating, tobacco smoking, and exercise), and if this relationship is mediated by patient confidence in their ability to care for themselves and by their self-perceived health status. Findings indicate that the use of health information, at low levels, is important for improving patient attitudes regarding their health status and confidence in caring for themselves. Perceived health-status and patient confidence, in turn, are associated with preventative health behaviors. Notably, too much information does not necessarily result in positive health behaviors among patients. Organizations may wish to critically assess how much information they make readily available to the populations they serve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".