The Effects of the Transforming Care at the Bedside Program on Perceived Team Effectiveness and Patient Outcomes
Bibliographic record
Abstract
The objective of the study was to document the impact of Transforming Care at the Bedside (TCAB) program on health care team's effectiveness, patient safety, and patient experience. A pretest and posttest (team effectiveness) and a time-series study design (patient experience and safety) were used. The intervention (the TCAB program) was implemented in 8 units in a multihospital academic health science center in Montreal, Quebec, Canada. The impact of TCAB interventions was measured using the Team Effectiveness (TCAB teams, n = 50), and Clostridium difficile-associated diarrhea and vancomycin-resistant Enterobacter rates (patient safety) and Hospital Consumer Assessment of Healthcare Providers and Systems (patient experience; n = 551 patients). The intervention was composed of 4 learning modules, each lasting 12 to 15 weeks of workshops held at the start of each module, combined with hands-on learning 1 day per week. Transforming Care at the Bedside teams also selected 1 key safety indicator to improve throughout the initiative. Pretest and posttest differences indicate improvement on the 5 team effectiveness subscales. Improvement in vancomycin-resistant Enterococcus rate was also detected. No significant improvement was detected for patient experience. These findings call to attention the need to support ongoing quality improvement competency development among frontline teams.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".