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Record W4361287245 · doi:10.1186/s13012-022-01259-8

Safer Care for Older Persons in (residential) Environments (SCOPE): a pragmatic controlled trial of a care aide-led quality improvement intervention

2023· article· en· W4361287245 on OpenAlexafffund
Adrian Wagg, Matthias Hoben, Liane Ginsburg, Malcolm Doupe, Whitney Berta, Yuting Song, Peter Norton, Jennifer Knopp‐Sihota, Carole A. Estabrooks

Bibliographic record

VenueImplementation Science · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsAthabasca UniversityUniversity of CalgaryUniversity of ManitobaYork UniversityPublic Health OntarioUniversity of TorontoUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineStaffingIntervention (counseling)NursingQuality managementHealth services researchLong-term careEmpowermentRandomized controlled trialQuality (philosophy)Family medicinePublic healthService (business)

Abstract

fetched live from OpenAlex

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 for residents. 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: The Safer Care for Older Persons in (residential) Environments (SCOPE) intervention 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. This study highlights the problem with measures drawn from current LTC databases to capture change in this population. Importantly, findings from the trial's concurrent process evaluation provide important insights into interpretation of main trial data, highlight the need for such evaluations of complex trials, and suggest the need to consider more broadly what constitutes "success" in complex interventions. TRIAL REGISTRATION: ClinicalTrials.gov , NCT03426072, registered August 02, 2018, first participant site April, 05, 2018.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.071
GPT teacher head0.515
Teacher spread0.444 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2023
Admission routes2
Has abstractyes

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