SCOPE: Safer Care for Older Persons (in Residential) Environments: A Single Arm Pilot Study
Bibliographic record
Abstract
Abstract Background: Nursing home residents require daily support. While care aides provide most of this support they are rarely empowered to lead quality improvement (QI) initiatives. A previous proof of principle study, called Safer Care for Older Persons in Residential Care Environments (SCOPE), demonstrated that care aide-led teams can successfully participate in QI interventions. In preparation for a large-scale study, this one-year pilot evaluated how well the bundle of SCOPE coaching strategies helped care-aide led teams to enact these interventions. A secondary aim was to determine if improvements in resident quality of care occurred. Methods: Using a modified IHI Breakthrough Collaborative Series model in a prospective single-arm study design, we randomly sampled 7 nursing homes in Winnipeg, Manitoba from the longitudinal Translating Research in Elder Care (TREC) cohort. Each SCOPE team had 5-7 front-line staff led by care aides. Teams received coaching to enact the intervention (i.e., to create actionable aim statements, implement QI interventions using plan-do-study-act [PDSA] cycles, use measurement to guide decision making) during three learning congresses, networked and shared learning experiences during these sessions, and received additional support from quality advisors between congresses. We used self-report data to code intervention enactment (‘poor’, ‘adequate’, ‘excellent’), and also measured improvement in team cohesion and communication. Secondarily, we observed changes in unit-level quality indicators using RAI-MDS 2.0 data.Results: Most teams successfully enacted SCOPE. Five of 7 teams created adequate-to-excellent aim statements throughout the pilot (e.g., statements were specific, measurable, time-bound). While 6 of 7 teams successfully implemented PDSAs, only 2 reported spreading their idea to involve more than a few residents and/or staff on their unit. Three of 7 teams explicitly stated how measurement was used to guide decisions. Team cohesion and communication scored high at baseline, and hence improved minimally. Resident quality indicators improved in 4 of the 7 nursing home units. Conclusions: Our bundled coaching strategies helped most care aide-led teams to enact SCOPE. Coaching modifications are needed in follow-up studies to help teams more effectively use measurement, and to spread successful interventions within the unit. More detailed and robust approaches are also needed to monitor treatment enactment.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".