Effective Strategies to Spread Redesigning Care Processes Among Healthcare Teams
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to describe how spread strategies facilitate the successful implementation of the Transforming Care at the Bedside (TCAB) program and their impact on healthcare workers and patients in a major Canadian healthcare organization. DESIGN: This study used a qualitative and descriptive design with focus groups and individual interviews held in May 2014. Participants included managers and healthcare providers from eight TCAB units in a university health center in Quebec, Canada. The sample was composed of 43 individuals. METHODS: The data were analyzed using NVivo according to the method proposed by Miles and Huberman. FINDINGS: The first two themes that emerged from the analysis are related to context (organizational transition requiring many changes) and spread strategies for the TCAB program (senior management support, release time and facilitation, rotation of team members, learning from previous TCAB teams, and engaging patients). The last theme that emerged from the analysis is the impact on healthcare professionals (providing front-line staff and managers with the training they need to make changes, team leadership, and increasing receptivity to hearing patients' and families' needs and requests). CONCLUSIONS: This study describes the perspectives of managers and team members to provide a better understanding of how spread strategies can facilitate the successful implementation of the TCAB program in a Canadian healthcare organization. CLINICAL RELEVANCE: Spread strategies facilitate the implementation of changes to improve the quality and safety of care provided to patients.
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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 | 0.023 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".