‘There is always a way to living with illness’—Self‐management strategies reported by Chinese hospitalized patients with cardiovascular disease: A descriptive qualitative study
Bibliographic record
Abstract
OBJECTIVES: Patients living with cardiovascular diseases use different strategies to solve various problems. This study aimed to identify the category, type and specific self-management strategies reported by hospitalized patients with cardiovascular diseases. METHODS: This is a qualitative descriptive study. Twenty-eight individuals with cardiovascular diseases from a Cardiology Department affiliated with a school in China were recruited by purposive sampling. Face-to-face semi-structured interviews were used. The interviews were audio-recorded, transcribed, translated and analysed by using content analysis. RESULTS: Five self-management strategy categories (medical and alternative therapy uptake, risk assessment and avoidance, resource seeking and utilization, maintaining normality, and optional management), and seventeen self-management strategy types, encompassing one hundred and ten specific strategies were identified. The most commonly used self-management strategy types were lifestyle adjustment (eleven strategies), self-maintenance (nine strategies) and problem-solving (nine strategies). Additionally, the most described explicit self-management strategies were receiving family/colleague support, maintaining daily routines, monitoring symptoms and managing side effects, discussing with professionals, using medicines, and improving awareness. CONCLUSION: This study identified diverse strategies reported by some Chinese cardiovascular patients. It may inform the design and development of personalized self-management interventions for health practitioners and policymakers, helping cardiovascular patients in Chinese communities worldwide receive culture-tailored services.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".