Factors involved in the development of hospital-acquired conditions in older patients in acute care settings: a scoping review
Bibliographic record
Abstract
BACKGROUND: Older patients hospitalized in acute care settings are at significant risk of presenting hospital-acquired conditions. Healthcare professionals should consider many factors involved in the development of such conditions, including factors related to the patients, as well as those related to the processes of care and the structure of hospitals. The aim of this study was to describe and identify the factors involved in the development of hospital-acquired conditions in older patients in acute care settings. METHODS: A scoping review was performed based on a structured search in eight databases in September 2022. Data were extracted with an extraction tool and classified into categories. Mapping and a narrative summary were used to synthetize data. RESULTS: A total of 237 articles were included in the scoping review. Functional decline and delirium were the most frequent hospital-acquired conditions studied. Among all categories, factors related to the patients provided most of the data, whereas factors related to the processes of care and the structure of hospitals were less frequently explored. In most articles, one or two categories of factors were retrieved; fewer articles examined factors among three categories. Personal factors, medications, and the human and work environment were the most frequent subcategories of factors retrieved, whereas social factors, hydration and nutrition, and organizational factors were less common. CONCLUSIONS: The development of hospital-acquired conditions in older patients in acute care settings involves many factors related to the patients, as well as to the processes of care and the structure of hospitals. Prevention of hospital-acquired conditions must involve to consider the complexities of older patients and of acute care hospitals. Not considering all categories of factors might affect the implementation of new practices of care and interventions.
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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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".