Managing Long-Term Care Staff Workload Through a Resident-Centred Intervention: A Mixed-Method Evaluation of the Synergy Tool
Bibliographic record
Abstract
Context: Long-term care (LTC) staff have been burdened by heavy workloads, compromising their ability to deliver resident-centred care. Objectives: This study adapted the Synergy Model for LTC and evaluated the impact from usage of the Synergy tool, a resident-needs assessment tool, on LTC staff perspectives. The tool assesses eight resident acuity and dependency characteristics and can be used in real-time to identify residents’ priority care needs and inform staffing and workload distribution. Methods: A mixed-method study was conducted in two Canadian LTC homes where the impact of the implementation of the Synergy tool was assessed. Quantitative data included online staff surveys (2 time points) and administrative overtime data (18 months). Qualitative data were gathered through two focus groups per LTC home with staff and leadership (n = 3–5 participants per focus group, N = 14 total participants). Findings: Quantitative data indicated mental health improvement and short-term decline in staff overtime rates during implementation. Qualitative data themes were improved care planning and resident-centred staffing. However, increased workload was perceived with short-term tool use. Limitations: Adoption of the tool was implemented over a short duration with a minimal impact strategy in a limited number of care units within the natural LTC setting to reduce overall LTC workload impact and to more closely mimic what would be feasible in most LTC homes. The findings from this study should be interpreted with the implementation constraints in mind. Implications: Although tool use yielded positive outcomes, structural barriers must be addressed to ensure successful implementation and sustainability.
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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 | 0.026 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".