eHealth, Participatory Medicine, and Ethical Care: A Focus Group Study of Patients’ and Health Care Providers’ Use of Health-Related Internet Information
Bibliographic record
Abstract
BACKGROUND: The rapid explosion in online digital health resources is seen as transformational, accelerating the shift from traditionally passive patients to patients as partners and altering the patient-health care professional (HCP) relationship. Patients with chronic conditions are increasingly engaged, enabled, and empowered to be partners in their care and encouraged to take responsibility for managing their conditions with HCP support. OBJECTIVE: In this paper, we focus on patients' and HCPs' use of health-related Internet information and how it influences the patient-HCP relationship. In particular, we examine the challenges emerging in medical encounters as roles and relationships shift and apply a conceptual framework of relational ethics to examine explicit and nuanced ethical dimensions emerging in patient-HCP interactions as both parties make increased use of health-related Internet information. METHODS: We purposively sampled patients and HCPs in British Columbia, Canada, to participate in focus groups. To be eligible, patients self-reported a diagnosis of arthritis and at least one other chronic health condition; HCPs reported a caseload with >25% of patients with arthritis and multimorbidity. We used a semistructured, but flexible, discussion guide. All discussions were audiotaped and transcribed verbatim. Elements of grounded theory guided our constant comparison thematic analytic approach. Analysis was iterative. A relational ethics conceptual lens was applied to the data. RESULTS: We recruited 32 participants (18 patients, 14 HCPs). They attended seven focus groups: four with patients and three with rehabilitation professionals and physicians. Predominant themes to emerge were how use of health-related Internet information fostered (1) changing roles, (2) patient-HCP partnerships, and (3) tensions and burdens for patients and HCPs. CONCLUSIONS: Relational aspects such as mutual trust, uncertainty, and vulnerability are illuminated in patient-HCP interactions around health-related Internet information and the negotiated space of clinical encounters. New roles and associated responsibilities have key ethical dimensions that make clear the changes are fundamental and important to understand in ethical care. When faced with tensions and burdens around incorporating health-related Internet information as a resource in clinical encounters, participants described a particular ambivalence illustrating the fundamental changes being negotiated by both patients and HCPs.
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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 | 0.036 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".