AUSTRALIAN PATIENTS USING A CARDIAC-DIABETES WEB-BASED INTERVENTION PROGRAM: CONTRIBUTIONS TO PERSON-CENTERED CLINICAL PRACTICE
Bibliographic record
Abstract
Rationale, aims and objectives: Patients with both cardiac disease and diabetes have poorer health outcomes than patients with only one chronic condition. While evidence indicates that internet based interventions may improve health outcomes for patients with a chronic disease, there is no literature on internet programs specific to cardiac patients with comorbid diabetes. Therefore this study aimed to develop a specific web-based program, then to explore patients’ perspectives on the usefulness of a new program.Methods: The interpretive approach using semi-structured interviews on a purposive sample of eligible patients with type 2 diabetes and a cardiac condition in a metropolitan hospital in Brisbane, Australia. Thematic analysis was undertaken to describe the perceived usefulness of a newly developed Heart2heart webpage.Results: Themes identified included confidence in hospital health professionals and reliance on doctors to manage conditions. Patients found the webpage useful for managing their conditions at home.Conclusions: The new Heart2heart webpage provided a positive and useful resource. Further research on to determine the potential influence of this resource on patients’ self-management behaviours is paramount. Implications for practice include using multimedia strategies for providing information to patients’ comorbidities of cardiac disease and type 2 diabetes, and further development on enhancement of such strategies.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".