Frailty Focused Enhancements to Seniors’ Hospital Care (FrESH): a Mixed Methods Study Reporting the Efficacy of Specialized Education for Front-line Staff
Bibliographic record
Abstract
Background: Acute care hospital stays often lead to increased frailty and functional decline in older adults. Interventions such as specialized education for nurses can improve health outcomes and decrease lengths of stay for these patients. This study aimed to identify the facilitators and barriers to providing care to older adults in acute care, and the efficacy of specialized education for front-line staff. Methods: A specialized education program for front-line staff, Frailty Focused Enhancements to Seniors' Hospital Care (FrESH), was developed and delivered across five family medicine units in New Brunswick (NB). A mixed methods approach was used to assess the knowledge, attitudes, and experiences of staff caring for hospitalized older adults, and evaluate the impact of providing specialized education. Patient-level data on delirium, mobility, and medications pre- and post-specialized education intervention were collected and analyzed. Results: Sixty-three front-line staff participated. Analysis of questionnaires demonstrated that staff had positive attitudes and beliefs about caring for older adults; however, knowledge of geriatric care principles was limited and remained unchanged. There was no significant change in patient-level measures post-intervention. Environmental constraints hindered staff from implementing best practices, leading to practical challenges to care delivery. While respondents expressed satisfaction with the education, their capacity to deliver the type of care presented in the education sessions was not achievable. Conclusion: Staff identified the need for specialized education; however, there was no impact on care after participation. Results will inform changes to the specialized education programs targeting care for hospitalized older adults in acute care.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".