<p>Chinese Hospitalized Cardiovascular Patients’ Attitudes Towards Self-Management: A Qualitative Study</p>
Bibliographic record
Abstract
PURPOSE: This study is aiming to investigate cardiovascular patients' attitudes towards self-management during hospitalization in China. PATIENTS AND METHODS: Twenty-nine individuals living with cardiovascular disease from one designated Cardiology Department in Hangzhou, China, were recruited through a purposive sampling procedure. A qualitative descriptive methodology was used. Semi-structured interviews were also used to gain attitudes toward self-management. The interviews were audio-recorded, transcribed and analyzed by thematic analysis to develop the results. RESULTS: Four themes were identified from the qualitative data: (1): Responsibilities of self-management; (2): Reflections on self-management; (3): Acknowledgement of self-management support; (4): Challenges in implementing and adherence to self-management. Additionally, interview data were also given to illustrate these main themes emerging during the analysis. Patients gradually took their responsibilities to manage chronic symptoms. During their self-management process, they did reflections to help correct their regiments through supportive interactions. Health system responsiveness, health disparities, social capital, and cultural setting were the main external factors influencing better self-management implementation and adherence. CONCLUSION: This study revealed the hospitalized cardiovascular patients' attitudes towards self-management in China. These findings emphasized the importance of patients' responsibility, reflections, and various social support receiving and pointed out specific external factors influencing the health outcomes and their quality of life. This study also proves the guide for the policymakers and health system better instructions to develop individually and culturally tailored advanced self-management interventions and programs.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".