Factors influencing nurses’ readiness to care for hospitalised older people
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To gain a better understanding of nurses' perspectives on factors that influence their readiness to provide appropriate care for hospitalised older people. BACKGROUND: Hospitals have consistently been criticised for failing to address the unique, complex needs of older people. Research suggests that multiple issues have led to this situation, including a lack of educational preparation for nurses, limited attention to environmental factors, and an absence of organisational preparedness that ensures hospitals are adapted to meet the needs of older people. DESIGN: An exploratory, qualitative approach was used. METHODS: Forty-one Registered Nurses participated (24 point-of-care nurses; 17 organisational leaders). Six focus groups and one individual interview were conducted. Thematic data analysis was employed to generate the main study findings. RESULTS: An overarching theme of 'Poor Fit' emerged. While participants identified the shifting needs of patients towards more complex and relational care, the broader organisational and societal contexts were, largely, unchanging. This resulted in nurses recognising the factors needed to be ready to care for older patients and their families, but working in hospitals that were not suited to these needs. CONCLUSIONS: The findings identify factors at the point-of-care, the organisational level, and in broader societal attitudes that shape nurses' readiness to care for hospitalised older people. However, many of these factors are modifiable and care for older people could be improved through quality improvement initiatives and nursing leadership. This study offers insight into ways to re-imagine nursing care that can be responsive to older people's complex needs in hospitals. IMPLICATIONS FOR PRACTICE: With a growing contingent of hospitalised older people, it is imperative that nurses, who comprise the largest workforce in this setting, be included in the planning and delivery of healthcare services to ensure readiness to meet the needs of this population.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".