Pilot implementation of elder-friendly care practices in acute care setting: a mixed methods study
Bibliographic record
Abstract
Abstract Background Frail older patients are at risk of experiencing a decline in physical and cognitive function unrelated to the reason for admission. The Elder-Friendly Care (EFC) program was designed to improve the care, experiences, and outcomes of frail older adults. The project supported 8 Early Adoption Sites (EAS) in a large Canadian healthcare organization by providing multiple strategies, educational opportunities, and resources. The purpose of this study was to assess the usefulness of EFC educational materials and resources, staff practice changes and perceptions in pilot sites, and readiness for scale and spread. Methods The study was conducted from May 2017 to June 2018 using a mixed-methods approach incorporating the Kirkpatrick Model of Training/Evaluation. A total of 76 Direct Care Staff participated in the staff survey, which assessed their awareness of, satisfaction with, and utilization of EFC principles, resources, and practices. Additionally, 12 interviews were conducted with staff who were directly involved in site implementation of EFC. Results Most survey participants were aware (86%, n = 63) of the EFC program, and 85% (n = 41) indicated they or their site/unit had implemented EFC. Out of these 41 participants, the most common practice changes identified were: incorporating alternatives to restraint (81%, n = 33), decreased use of pharmacological restraint (78%, n = 32), and patient and family care planning (76%, n = 31). Participants that attended all 3 EFC Learning Workshops (LWs) were significantly more likely to recommend the EFC Toolkit to others (87% versus 40%; χ2 = 8.82, p
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".