Patient-centred assessment of social support, health status and quality of life in patients with acute coronary syndrome.
Bibliographic record
Abstract
BACKGROUND: Measurement of health status (HS) and social support are becoming increasingly accepted as tools to guide clinical decision-making and patient-centred practice. PURPOSE: To assess self-reported HS, cardiac-health related quality of life and social support in subjects with a diagnosis of acute coronary syndrome (ACS). DESIGN: The study used a quantitative descriptive design. SAMPLE: 36 subjects with a diagnosis of ACS were selected from patients admitted to medical units at a teaching hospital in Toronto, Ontario. METHODS: One-time, semi-structured interviews were conducted using valid and reliable cardiac-specific HS and social support measures. RESULTS: Analysis indicated that subjects with higher perceived social support and patients with higher income reported greater treatment satisfaction and C-HROL. Subjects with severe angina reported a higher perceived level of social support than those with more moderate physical limitation due to angina. CONCLUSION: Patients' social environment and HS significantly impact their satisfaction with treatment. Patient-centred measures assist in clinical decision-making, patient-centred care planning and patient involvement in their care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".