Exploring eHealth Ethics and Multi-Morbidity: Protocol for an Interview and Focus Group Study of Patient and Health Care Provider Views and Experiences of Using Digital Media for Health Purposes
Bibliographic record
Abstract
BACKGROUND: eHealth is a broad term referring to the application of information and communication technologies in the health sector, ranging from health records to medical consultations (telemedicine) and multiple forms of health education, support, and tools. By providing increased and anytime access to information, opportunities to exchange experiences with others, and self-management support, eHealth has been heralded as transformational. It has the potential to accelerate the shift from traditional "passive patient" to an informed, engaged, and empowered "patient as partner," equipped to take part in shared decision-making, and take personal responsibility for self-managing their illness. OBJECTIVE: The objective of our study is to examine how people with chronic illness use eHealth in their daily lives, how it affects patient-provider relationships, and the ethical and practical ramifications for patients, providers, and service delivery. METHODS: This two-phase qualitative study is ongoing. We will purposively sample 60-70 participants in British Columbia, Canada. To be eligible, patient participants have to have arthritis and at least one other chronic health condition; health care providers (HCPs) need a caseload of patients with multi-morbidity (>25%). To date we have recruited 36 participants (18 patients, 18 HCPs). The participants attended 7 focus groups (FGs), 4 with patients and 3 with rehabilitation professionals and physicians. We interviewed 4 HCPs who were unable to attend a FG. In phase 2, we will build on FG findings and conduct 20-24 interviews with equal numbers of patients and HCPs (rehabilitation professionals and physicians). As in the FGs conducted in phase I, the interviews will use a semistructured, but flexible, discussion guide. All discussions are being audiotaped and transcribed verbatim. Constant comparisons and a narrative approach guides the analyses. A relational ethics conceptual lens is being applied to the data to identify emergent ethical issues. RESULTS: This study explores ethical issues in eHealth. Our goal is to identify the role of eHealth in the lives of people with multiple chronic health conditions and to explore how eHealth impacts the patient role, self-managing, and the patient-HCP relationship. The ethical lens facilitates a systematic critical analysis of emergent ethical issues for further investigation and pinpoints areas of practice that require interventions as eHealth develops and use increases both within and outside of the clinical setting. CONCLUSIONS: The potential benefits and burdens of eHealth need to be identified before an ethical framework can be devised.
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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 | 0.055 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".