The care of older adults in hospital: if it's common sense why isn't it common practice?
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To present three converging myths that underlie the perception that nursing care for older persons in hospital is simple in comparison with its actual complexity. BACKGROUND: Literature provides strong evidence indicating that the myths we discuss inherently arise from ageism, the social value of older patients and the economic burden of being an older patient in hospital. These powerful social discourses promote harm to older patients. Harm emerges from both the omission of gerontological nursing knowledge and skill and the commission of acts that unintentionally harm. A corresponding ethical challenge results for acute care nurses. DESIGN: A discursive paper. METHODS: We illuminate gerontological issues by discussing three myths. In myth one, we detail four clusters of distinguishing characteristics in older hospitalised people. In the second myth, we challenge the idea that the role of the acute care hospital is to attend only to acute medical concerns. Finally, in the third myth, we address the issue of incorporating functional assessment into the acute care nursing assessment. We argue that functional assessment is poorly integrated and becomes acceptable only as long as the medical regimen is managed appropriately. CONCLUSION: Safe quality care in hospital for older adults requires a hybrid practice that integrates acute care specialty knowledge with gerontological nursing knowledge and skill. Clinical reasoning that integrates this type of nursing knowledge can prevent harm. RELEVANCE TO CLINICAL PRACTICE: Integrating key elements of acute care nursing specialty knowledge with gerontological nursing principles aids to prevent the omission of care that is known to be harmful to older people. Conversely, the commission of gerontologically sensitive acute care can enhance safety and promote quality care in hospital.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".