Supporting self-management in patients with congestive heart failure
Bibliographic record
Abstract
Heart failure is the most common cause of hospitalization in Canada. Over half of the hospitalized patients are readmitted within 6 months of discharge. These patients face even greater health risks as a result of gaps in care which occur during transition of care from hospital to community. The research presented in this dissertation builds on the existing body of research on transition of care by identifying issues that patients face after discharge from hospital and by proposing and evaluating a remote self-care education program. In the first part of this thesis, methodologies relevant to development and evaluation of information systems for healthcare are considered, and an alternative framework is proposed. Consistent with the proposed framework, patient self-management education, the educational approach which forms the basis of the proposed intervention, is validated through a systematic review of literature. Next, two qualitative studies were conducted in order to identify issues that occur during transition of care, from the perspectives of healthcare practitioners and patients. The first study consisted of a contextual inquiry and interviews with case managers. In the second qualitative study, patients with heart failure who were discharged to the community were interviewed and barriers and enablers to care that affect this patient population were identified. Based on the feedback from the systematic review and the qualitative studies, an intervention consisting of remote self-management education was designed. In a randomized study involving patients with heart failure released from hospitals in the Greater Toronto Area, two methods of delivery of self-care education were compared. One group was given an access to a web-site containing educational content, and another group was also participating in four live telephone-based education sessions led by a cardiac nurse. Results demonstrated that patients in the live education groups scored higher on the self-care knowledge test following the intervention. This dissertation concludes with a distinction of the implications of this research for future care of congestive heart failure patients.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".