Safer Care for Older Persons in (residential) Environments: a pragmatic controlled trial of a care aide-led quality improvement intervention.
Bibliographic record
Abstract
Abstract Background: The increased complexity of residents and increased needs for care in long term care (LTC) have not been met with increased staffing. There remains a need to improve the quality of care. Care aides, providers of the bulk of direct care, are well placed to contribute to quality improvement efforts but are often excluded from so doing. This study examined the effect of a facilitation intervention enabling care aides to lead quality improvement efforts and improve the use of evidence-informed best practices. The eventual goal was to improve both the quality of care for older residents in LTC homes and the engagement and empowerment of care aides in leading quality improvement efforts.Methods: Intervention teams participated in a year-long facilitative intervention which supported care aide-led teams to test changes in care provision to residents using a combination of networking and QI education meetings, and quality advisor and senior leader support. This was a controlled trial with random selection of intervention clinical care units matched 1:1 post hoc with control units. The primary outcome, between group change in conceptual research use (CRU), was supplemented by secondary staff- and resident- level outcome measures. A power calculation based upon pilot data effect sizes resulted in a sample size of 25 intervention sites. Results: The final sample included 32 intervention care units matched to 32 units in the control group. In an adjusted model, there was no statistically significant difference between intervention and control units for CRU or in secondary staff outcomes. Compared to baseline, resident adjusted pain scores were statistically significantly reduced (less pain) in the intervention group (p=0.02). The level of resident dependency significantly decreased statistically for residents whose teams addressed mobility (p<0.0001) compared to baseline. Conclusions: SCOPE resulted in a smaller change in its primary outcome than initially expected resulting in a study underpowered to detect a difference. These findings should inform sample size calculations of future studies of this nature if using similar outcome measures. Importantly, findings from the concurrent process evaluation provided important insights into interpretation of main trial data and suggest the need to consider more broadly what constitutes “success” in complex interventions.Trial registration: clinicaltrials.gov: NCT03426072 (https://clinicaltrials.gov/ct2/show/NCT03426072), registered 08/02/2018, first participant site 04/05/2018.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".