The influence of country of origin on engagement in self‐care behaviours following heart surgery: a descriptive correlational study
Bibliographic record
Abstract
AIM: The aim of this study was to determine whether an individual's country of origin influenced performance of self-care behaviours after heart surgery. BACKGROUND: Patients are required to perform self-care behaviours following cardiovascular surgery. Usual care encompasses a patient education initiative that addresses self-care behaviour performance. Within Canada, current heart surgery patient education efforts have been designed and evaluated using homogenous samples that self-identify their country of origin as England, Ireland or Scotland. However, approximately 42·6% of Canadian cardiovascular surgical patients self-identify their country of origin as India or China. Thus, current cardiovascular surgery patient education initiatives may not be applicable to all patients undergoing heart surgery, which may result in decreased patient outcomes such as performance of self-care behaviours. DESIGN: This descriptive study. METHODS: A convenience sample of 90 patients who underwent heart surgery at one of two university-affiliated teaching hospitals, representing individuals of diverse backgrounds. Point-biserial correlational analysis was conducted to determine the relationship between country of origin and performance of self-care behaviours. RESULTS: Findings indicate individuals who self-identified their country of origin as England or Ireland were associated with a higher score on the number of self-care behaviours performed (p < 0·05) than individuals who self-identified other countries of origin. Self-care behaviours were taught using patient education materials that were designed based on feedback obtained from individuals whose country of origin was England or Ireland. CONCLUSION: This study provides preliminary evidence to suggest country of origin influences the amount of self-care behaviours individuals will perform. RELEVANCE TO CLINICAL PRACTICE: Patient education initiatives should incorporate the values, beliefs, attitudes and customs reflective of an individual's country of origin to enhance the likelihood of producing desired outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".