(Re)conceptualising Good Care in Hospital Settings From the Perspectives of Older Persons: A Concept Analysis Using Pragmatic Utility
Bibliographic record
Abstract
PURPOSE: To understand good care from the perspective of hospitalised older persons. BACKGROUND: Older persons are the largest group of hospital users, and numbers will increase in the next decades. Hospital organisations are attempting to transform traditional care models to meet the specialised needs of hospitalised older persons. To achieve this, healthcare providers including nurses and administrators need to understand the perspectives of hospitalised older persons on what constitutes good care. This knowledge is critical to nursing to ensure that care aligns with the perceived needs of hospitalised older persons. However, good care from the viewpoints of hospitalised older persons remains ambiguous and poorly delineated in the literature. METHODS: We conducted a concept analysis using the pragmatic utility method. To identify peer-reviewed articles, we searched CINAHL, MedLine, PsycINFO, Scopus and Embase databases for related literature using the keywords and related terms to 'good care', 'hospital or acute care' and 'older persons'. RESULTS: Twenty-two peer-reviewed articles out of 2144 search results were included. The heterogeneity of older persons' perspectives and limitations in the literature on good care led to a tentative understanding. Good care, a partially mature concept, is the provision of person-centred, culturally sensitive, holistic and integrated care that fosters autonomy, control and participation, resulting in a sense of belonging, smooth transitions, optimal management of clinical conditions, satisfaction in care and informed older persons and family caregivers. CONCLUSION: Older persons are a heterogeneous group with diverse perceptions of good care. Instead of seeking a common understanding of good care, efforts should be focused on identifying individual preferences, values and goals of hospitalised older persons. IMPLICATIONS FOR PRACTICE: The components of good care are important for many hospitalised older persons and can serve as a starting point for improvements in practice settings. However, this understanding is tentative and may overlook critical aspects of care at an individual level. Hospital organisations, healthcare providers and nurses should be cognizant of this limitation and cultivate adaptability for an individualised approach to 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 | 0.035 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| 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".