Joint replacement recipients' views about health information privacy
Bibliographic record
Abstract
BACKGROUND: Researchers are concerned about the possibility of restricted access to data as a result of specific consent requirements in privacy legislation, potentially resulting in smaller samples and a lack of representativeness which could bias results. In addition, there is uncertainty about what influences individuals to give consent for the use of their personal health information. OBJECTIVE: To measure joint replacement recipients' health information privacy views and to assess potential predictors of these views. DESIGN: Cross-sectional survey. SETTING AND PARTICIPANTS: Potential joint replacement recipients from two teaching hospitals in London, Ontario, Canada. MAIN VARIABLES: Age, gender, education, employment status, anticipated joint replacement, and expectations for surgery. MAIN OUTCOME MEASURES: Privacy concerns as measured by the Concern Scale. RESULTS: The response rate was 182/253 or 72%. The mean Concern score was 143.9/235.0 for the total sample (range = 82-216). Women had higher levels of privacy concerns than men on slightly over half of the individual questionnaire items. In women, surgical joint, age and employment explained 15% of the variance in concerns about personal health information privacy (P = 0.001). The model explained 6% of the variance in concerns in men (P = 0.138) and was not statistically significant. DISCUSSION AND CONCLUSION: This study indicates that demographic characteristics and health-care experiences play a role in the variability of health information privacy concerns. A greater understanding of patients' privacy views about health information could lead to a greater harmonization among privacy rules, research and data access, and the preferences of health-care consumers.
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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 | 0.014 | 0.032 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".