FRAILTY-FOCUSED ENHANCEMENTS TO SENIORS’ HOSPITAL CARE (FRESH): DOES SPECIALIZED EDUCATION WORK?
Bibliographic record
Abstract
Abstract Research suggests that specialized education for nurses decreases frailty and improves functionality in hospitalized older adults. This study explored the impact of a specialized geriatric education program on care delivery for older adults in acute care in 5 hospitals across the province of New Brunswick. A mixed-methods approach with pre- and post- questionnaires was used to explore facilitators and challenges of caring for hospitalized older adults, the knowledge base and experiences of staff, and the impact of providing specialized education. Acute care staff (N=64) participated in a geriatric education intervention and completed pre and post questionnaires. A sub-set (n=26) participated in semi-structured interviews guided by Kirkpatrick’s theoretical framework. Data was collected on mobility, medications, and delirium from patients’ charts (N=99) to identify changes in outcomes pre- and post intervention. Findings revealed that staff had positive attitudes toward caring for older adults; however, their understanding and application of geriatric principles were limited and remained unchanged post-education. There was no significant change in the patient-level measures after the intervention. Interview participants shared that their work environment affects their mindset and workflow, limiting their capacity to deliver the type of care presented in the education sessions. Staff identified the need for specialized education; however, there was no impact on care after participation. Environmental constraints hindered staff from implementing best practices, leading to both practical and psychological challenges to care delivery. These results will inform changes to specialized education programming with the aim of improving care for older adults in hospital.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".