Nurses’ perspectives on how operational leaders influence function-focused care for hospitalised older people
Bibliographic record
Abstract
AIMS: To explore nurses' perspectives on how leaders influence function-focused care, defined as care that preserves and restores older people's functional abilities. BACKGROUND: Hospitalised older people are at risk of functional decline. Although leaders have the potential to influence function-focused care, few studies have explored nurses' perspectives on how leaders influence function-focused care. METHODS: Thirteen focus groups were held with 57 acute care nurses. Semi-structured questions prompted discussion on nurses' perspectives, needs and strategies to meet their needs. Data were thematically analysed. RESULTS: Three themes were identified: (1) the emphasis in hospitals is on moving older people quickly through the system, not supporting their functioning; (2) leaders are generally seen as too disconnected from practice to design system efficiency initiatives that support older people's functioning and nurses' provisioning of function-focused care; and (3) leadership strategies to better support nurses in providing function-focused care to older people in the context of system efficiency. CONCLUSIONS: Leaders should connect with practice to devise age-sensitive efficiency initiatives that support function-focused care. Nurses need support from leaders in four areas to provide function-focused care to older people in the current hospital context. IMPLICATIONS: The findings provide direction on how leaders can facilitate function-focused care in the current health-care environment emphasising system efficiency.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".