Examining the Impact of Knowledge Translation Interventions on Uptake of Evidence‐Based Practices by Care Aides in Continuing Care
Bibliographic record
Abstract
BACKGROUND: Dissemination of evidence-based practices has been a long-standing challenge for healthcare providers and policy makers. Research has increasingly focused on effective knowledge translation (KT) in healthcare settings. AIMS: This study examined the effectiveness of two KT interventions, informal walkabouts and documentation information sessions, in supporting care aide adoption of new evidence-based practices in continuing care. METHODS: The Sustaining Transfers through Affordable Research Translation (START) study examined sustainability of a new practice, the sit-to-stand activity completed with residents in 23 continuing care facilities in Alberta, Canada. At each facility, two informal walkabouts and two documentation information sessions were conducted with care aides during the first 4 months. To assess their effect, uptake of the sit-to-stand activity was compared 4 days prior to and 4 days after each intervention, as well as the entire first and fourth months of the study were compared. Data were analyzed using mixed linear regression models created to estimate the changes in uptake. RESULTS: Data were collected from 227 residents. After controlling for age, sex, dementia, and mobility, a 5.3% (p = .09) increase in uptake of the mobility activity was observed during the day shift and 6.1% (p = .007) increase in uptake of the mobility activity during the evening shift. Site size had a significant effect on the outcome with medium-sized facilities showing a 12.6% (SE = .07) increase over small sites and a 18.2% (SE = .05) increase over large sites. These results suggest that repeated KT interventions and sufficient time are key variables in the successful implementation of new practices. LINKING EVIDENCE TO ACTION: Consideration of time, repetition, and facility-specific variables such as size may generate simple, cost-effective KT interventions in healthcare settings.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".