Driving health behavior: The roles of personal health information use and attitudes towards health in women’s cancer screenings
Bibliographic record
Abstract
Objective: Our interest in patient attitudes and beliefs and how they contribute to health and health seeking behaviors is based on growing interest in fostering more patient-centered care. This is particularly relevant for cancer screening in women, where the procedures may be viewed as deeply personal and emotionally invasive. There is convincing evidence that health attitudes and beliefs are strong associates of cancer screening among women. The goal of this paper is to examine if accessibility and use of personal health information (PHI) is a positive predictive of cancer related health detection behaviors among United States women. This study is relevant and timely considering the growing focus on prevention in patient-centered care delivery.Methods: Using data from the 2017 Health Information National Trends Survey (HINTS), this paper employed multivariable path analysis to investigate whether PHI use is related to engaged women’s health detection behaviors, and if this relationship is mediated by self-perceived health status and patient attitudes regarding confidence in their self-care abilities.Results: This study found that PHI use worked directly on health detection behaviors for intermediate levels of health information only. Our findings also suggest that patient attitudes may only act as a mediator at low levels of information use and when both confidence in self-care abilities and self-assessed health status are considered simultaneously.Conclusions: As prevention continues to be a key focus of health care, efforts promoting enhanced population health are critically important. With greater expansion of patient portals, health systems and providers are expecting access to greater PHI will promote increased engagement by patients in their self-health. The results of our research suggest that PHI is positive for patients up until a point and that health care delivery professionals may wish to assess the amount and type of information made readily available to the patients they serve related to breast and cervical cancer screenings.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".