Feasibility of a patient decision aid regarding disclosure of personal health information: qualitative evaluation of the Health Care Information Directive
Bibliographic record
Abstract
BACKGROUND: Concerns regarding the privacy of health information are escalating owing both to the growing use of information technology to store and exchange data and to the increasing demand on the part of patients to control the use of their medical records. The objective of this study was to evaluate the Health Care Information Directive (HCID), a recently-developed patient decision aid that aims to delineate the level of health information an individual is willing to share. METHODS: We convened a series of four focus group meetings with several communities in a large Canadian city. A total of 28 men and women participated, representing health care consumer advocates, urban professionals, senior citizens, and immigrants who speak English as a second language. Data were analysed using qualitative methods. RESULTS: Participants lacked substantial knowledge regarding the fate and uses of personal health information. They expressed mistrust concerning how their information will be used and protected. Several suggestions were made towards customizing the use of data according to specific needs rather than broad and full access to their charts. Furthermore, despite concern regarding the implementation of a tool like the HCID, participants were hopeful that a refined instrument could contribute to the improved regulation of health information. CONCLUSION: This study indicated poor knowledge concerning the uses of personal health information, distrust concerning security provisions, and cautious support for a patient decision aid such as the HCID to improve control over health data.
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 | 0.103 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".