Socio-demographics and health profile: Influence on self-care
Bibliographic record
Abstract
Variability in the performance of self-care behaviours have been reported in the cardiovascular surgical population. Theoretical evidence suggest that demographic characteristics and health profile influence patients’ engagement in self-care behaviours. However, the influence of these variables on performance of self-care has not been examined. The purpose of this quantitative, non-experimental study was to determine how much variance in performance of self-care behaviours is accounted for by demographic characteristics, as well as the health profile of patients who underwent heart surgery. Data from a sample of 248 study participants, recruited from two cardiovascular surgical units, were collected. Descriptive statistics were used to characterize the sample on demographics and health profile, while multiple regression analysis was conducted to determine the relationship between variables. Findings suggest these factors have a minimal influence on self-care behaviour performance. Alternative factors influencing self-care behaviour performance were identified along with implications for future nursing practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".