What Do Patients Talk About? A Qualitative Analysis of Online Chat Sessions with Health Care Specialists During a “Virtual” Cardiac Rehabilitation Program
Bibliographic record
Abstract
INTRODUCTION: Cardiac rehabilitation programs (CRPs) are effective at reducing cardiovascular disease (CVD) risk, yet attendance in these programs remains low due to geographic constraints. In a previously conducted randomized trial we demonstrated that a virtual CRP (vCRP) delivered over the Internet reduced risk for CVD. The current investigation has reviewed the online chat sessions between participants and healthcare providers (HCP) to describe the content of discussions during the vCRP intervention. MATERIALS AND METHODS: Participants were recruited from two geographically isolated areas in British Columbia, Canada without in-person CRP or a cardiologist serving the area. The vCRP, among other elements, included scheduled one-on-one chat sessions with a dietician, exercise specialist, and nurse to mimic standard CRP consultations. The chat sessions were reviewed for content and themes. Multiple chat sessions between participants and a single care provider were also analyzed to describe how chat content progressed through multiple sessions. RESULTS: A total of 38 participants participated in the vCRP intervention. From the 122 chat sessions between participants and HCP during the vCRP, the main themes identified were Managing Health and Lifestyle, Continuity of Care, and Getting Care from a Distance. Within each theme, sub-themes were also identified. CONCLUSIONS: The vCRP chat sessions fulfilled the role of face-to-face consultations with HCP that are standard in hospital-based CRP and addressed patient concerns, facilitating remote patient-provider interaction and covering topics on exercise, diet, and positive behavior changes to limit risk factors for future heart problems.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".