Curriculum, Theory, and Practice: Exploring Nurses’ and Nursing Students’ Knowledge of and Attitudes towards Caring for the Older Adults in Canada
Bibliographic record
Abstract
BACKGROUND: Caring for older adults is among the most challenging issue of public health and social care systems in modern societies. By enhancing the nursing curriculum, nursing students will be qualified to provide gerontology care, and they will be acknowledging and working to eliminate ageism from the health care system. PURPOSE: This study explores nurses' and nursing students' knowledge and attitudes in caring for older adults and addresses the factors contributing to nurses' perspectives. It also examines the nursing curriculum's contributions to nurses' knowledge and attitudes and provides suggestions aimed at reconfiguring the nursing curriculum for comprehensive gerontology nursing care. METHODS: A mixed-method research design was utilized, and quantitative and qualitative data were collected from 90 nurses and nursing students through an online questionnaire. Data were analyzed via SPSS and NVivo 12 software programs. RESULTS: The results revealed that most nurses possess neutral attitudes toward caring for older patients, and their knowledge ranged from average to above-average levels. Statistical analysis revealed no statistically significant difference between gender and nurses' attitudes or between gender and knowledge. Similarly, there was no statistically significant difference between work status and nurses' attitudes. Results showed a statistically significant positive correlation between nurses' attitudes and knowledge level. This study demonstrated the positive impact of the Canadian nursing curriculum on nurses' knowledge and attitudes. CONCLUSION: The current study recommends providing gerontology nursing courses as a mandatory separate course in nursing education to enhance nursing students' knowledge and skills for high-quality gerontology nursing 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".