Orchestrating care: nursing practice with hospitalised older adults
Bibliographic record
Abstract
BACKGROUND: The increased incidence of health challenges with aging means that nurses are increasingly caring for older adults, often in hospital settings. Research about the complexity of nursing practice with this population remains limited. OBJECTIVE: To seek an explanation of nursing practice with hospitalised older adults. METHODS: Design. A grounded theory study guided by symbolic interactionism was used to explore nursing practice with hospitalised older adults from a nursing perspective. Glaserian grounded theory methods were used to develop a mid-range theory after analysis of 375 hours of participant observation, 35 interviews with 24 participants and review of selected documents. RESULTS: The theory of orchestrating care was developed to explain how nurses are continuously trying to manage their work environments by understanding the status of the patients, their unit, mobilising the assistance of others and stretching available resources to resolve their problem of providing their older patients with what they perceived as 'good care' while sustaining themselves as 'good' nurses. They described their practice environments as hard and under-resourced. Orchestrating care is comprised of two subprocesses: building synergy and minimising strain. These two processes both facilitated and constrained each other and nurses' abilities to orchestrate care. CONCLUSIONS: Although system issues presented serious constraints to nursing practice, the ways in which nurses were making meaning of their work environment both aided them in managing their challenges and constrained their agency. IMPLICATIONS FOR PRACTICE: Nurses need to be encouraged to share their important perspective about older adult care. Administrators have a role to play in giving nurses voice in workplace committees and in forums. Further research is needed to better understand how multidisciplinary teams influence care of hospitalized older adults.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".