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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".