Building a communication and support network among quality improvement teams in nursing homes: a longitudinal study of the SCOPE trial
Bibliographic record
Abstract
Abstract Background We applied a longitudinal network analysis approach to assess the formation of knowledge sharing and collaboration networks among care aide-led quality improvement (QI) teams in Canadian nursing homes participating in the Safer Care for Older Persons (in residential) Environments (SCOPE) trial which aimed to support unregulated front-line staff to lead unit-based quality improvement (QI) teams in nursing homes. We hypothesized that SCOPE's communicative and participatory nature would provide opportunities for peer support, knowledge sharing, and collaboration building among teams. Methods Fourteen QI teams in Alberta (AB) and seventeen QI teams in British Columbia (BC) participated in the study. Communications across nursing homes occurred through a series of 4 collaborative Learning Congresses (training sessions) over a 1-year period. The senior leaders of QI teams participated in two online network surveys about the communication/collaboration between teams in their province, 1 month after the first, and 6 months later, after the fourth Learning Congress. We developed communication and collaboration network maps pertaining to three time points: before SCOPE, at 2 months, and at 9 months. Results Over time, teams made significantly more new connections and strengthened existing ones, within and across regions. Geographic proximity and co-membership in organizational chains were important predictors of connectivity before and during SCOPE. Teams whose members were well connected at baseline disproportionately improved connectivity over time. On the other hand, teams that did not have prior opportunities to connect appeared to use SCOPE to build new ties. Conclusions Our findings suggest the importance of network-altering activities to the formation of collaboration networks among QI teams across nursing homes. Active strategies could be used to better connect less connected teams and facilitate collaboration among geographically proximate teams. These findings may inform the development of interventions to leverage existing networks and provide new networking opportunities to develop and sustain organizational improvements.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".