EFFECTIVENESS OF PEER-BASED REMINDERS TO SUSTAIN CARE PROVIDER PRACTICE CHANGE: A CLUSTER RCT
Bibliographic record
Abstract
Increasing demand for residential aged care has strained already tight healthcare budgets. Identifying novel strategies to optimize best-practice use in these settings may improve quality and efficiency of care and reduce caregiver burden. The purpose of this study was to assess the effect of peer-based and paper-based reminders targeting direct care providers to sustain a mobility innovation with older adults in residential care facilities. START (Sustaining Transfers through Affordable Research Translation) was a 23-site cluster-randomized controlled trial that took place in Alberta, Canada. A mobility intervention was introduced to 23 study sites; between March 2014 and April 2015, 11 sites were randomly assigned to receive paper reminders and a peer reminder intervention either monthly (n=5) or quarterly (n=6) for 1 year. The remaining 12 sites were randomly assigned to receive paper-reminders only either monthly (n=6) or quarterly (n=6) for 1 year. Direct care staff daily documented the uptake of the mobility intervention for 1 year in all 23 sites. Uptake data were analyzed using linear mixed models that mirrored the clustered repeated-measures factorial trial design. A statistically significant improvement was detected in sustainability of the mobility intervention in the sites receiving peer and paper reminder interventions, compared with sites receiving paper-only interventions (p = 0.007). No significant difference was detected in sustainability between monthly or quarterly implementation of either the paper or peer intervention (p = 0.72). This peer reminder intervention is an effective knowledge translation strategy to change and sustain care provider behaviour in residential care.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".