LEARN to co-manage heart failure: implementation of best practice guidelines
Bibliographic record
Abstract
Effective treatment of heart disease and an aging population have led to increases in the incidence of heart failure. Treatment requires complex medication regimes and recognition of symptoms. Best practice guidelines published by the American, European and Canadian cardiac societies promote self-care behaviours and skill building. No concrete examples of education programmes for clients were found in the literature. The purpose of the study was to develop, pilot and evaluate an education series for clients with heart failure within a primary care setting. “LEARN twice”, a three part education series with related resource material, was developed in the context of inter-professional collaboration and drew upon theories of health education, and literacy. The concept of co-management was incorporated as the philosophical basis in the design. The pilot-test used an experimental design, and incorporated pre and post-testing with standardized instrumentation including the Dutch Heart Failure Knowledge Scale and the Minnesota Living with Heart Failure Questionnaire. To pilot the education series, participants attended three education classes highlighting the essential skills for self management of heart failure. A qualitative descriptive component included brief semi-structured interviews with participants and educators to provide feedback about both the process and content of the educational series. Limited participant numbers did not permit statistical testing, however potentially promising results were found in the quantitative data collected. Descriptive participant data indicated that the education series was meaningful, and helped iii understanding of symptoms. Instructors rated the content as good to excellent and anticipate the adoption of the education series as standard practice in the clinic. The pilot test of the education series has provided a foundation for future research endeavours, in particular the replication and completion of this study protocol. As clients with heart failure have the potential to be in regular contact with a primary care provider, subsequent studies could include a longitudinal component to examine whether rates of re-hospitalization are reduced for clients who attend an education series.
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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 | 0.077 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".