The Impact of Relational Coordination on Staff Outcomes in Long-Term Care
Bibliographic record
Abstract
Abstract Relational coordination (RC) emphasizes the importance of communication and relationships among staff to effectively coordinate care. This study investigated the impact of RC on staff outcomes: work engagement, job satisfaction, and burnout in seven long-term care (LTC) homes in Ontario, Canada. In phase 1 of this mixed-methods study, an online survey was distributed to staff (nurses, allied health providers, personal support workers) working in LTC homes. Staff were asked about RC, work engagement, job satisfaction, work-related burnout (professional efficacy, emotional exhaustion, cynicism), and demographic information. The impact of RC on staff outcomes was assessed using random-effect modeling to account for the multi-level (staff/ LTC) data, with staff as the unit of analysis and LTC as the random effect. We received 153 completed surveys. RC had a significant positive association with staff work engagement, job satisfaction, and professional efficacy in both unadjusted and adjusted models for staff demographics and LTC size. In contrast, RC had a significant negative association with emotional exhaustion and a non-significant negative association with cynicism. Age had a significant positive association with work engagement and job satisfaction, while the association was inverse with cynicism. Holding a bachelor’s degree or higher had a significant positive association with work engagement and job satisfaction; however, the association was inverse with emotional exhaustion. None of the demographics were significantly associated with professional efficacy. Better communication and relationships between staff can enhance their work engagement and well-being, which could improve overall work efficiency and support quality improvement innovations for integrated 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".