Patient confidentiality within the context of group medical visits: is there cause for concern?
Bibliographic record
Abstract
BACKGROUND: Group medical visits (GMVs), clinical encounters with a medical component delivered to groups of patients, have emerged as an innovative approach to potentially increasing efficiency while enhancing the quality of primary health care (PHC). GMVs have created the need to pay explicit attention to patient confidentiality. OBJECTIVE: What strategies are used by providers and patients to address issues of confidentiality within GMVs? DESIGN: In-depth interviews were conducted with 34 PHC providers and 29 patients living in nine rural communities in British Columbia, Canada. Data were analysed using interpretive thematic analysis and a relational autonomy approach. RESULTS: We found three main themes: (i) choosing to disclose: balancing benefits and drawbacks of GMVs, (ii) maintaining confidentiality in GMVs and (iii) gaining strength from interdependent relationships: patients learning from each other. Confidentiality can be addressed and was not a major concern for patients attending or providers facilitating GMVs in these rural communities. DISCUSSION: Patients adopted strategies to address their own and others' concerns related to confidential health information. Providers used multiple strategies to maintain confidentiality within the group, including renegotiating what information is shared and providing examples of what information ought to be kept confidential. CONCLUSIONS: Although GMVs are not for all patients, a relational autonomy approach is useful in drawing attention to the context and structures which may influence their patients' ability to act autonomously. Successful delivery of GMVs requires both patients and providers to negotiate between maintaining confidentiality and an appropriate level of disclosure.
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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 | 0.035 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".