The Effect of Transforming Care at the Bedside Initiative on Healthcare Teams’ Work Environments
Bibliographic record
Abstract
BACKGROUND: Different initiatives have been implemented in healthcare organizations to improve efficiency, such as transforming care at the bedside (TCAB). However, there are important gaps in understanding the effect of TCAB on healthcare teams' work environments. AIM: The specific aim of the study is to describe findings regarding the TCAB initiative effects on healthcare teams' work environments. METHODS: A pretest and posttest study design was used for this study. The TCAB initiative was implemented in fall 2010 in a university health center in Montreal, Canada. The sample consisted of healthcare workers from four different care units. RESULTS: Statistically significant improvement was observed with the communicating specific information subscale from the measure of processes of care variable, and a significant difference was found between the support from colleagues variable, which was higher at baseline than postprogram. The differences for psychological demand, decisional latitude, and effort-reward were not significant. CONCLUSIONS: TCAB is an intervention that allows healthcare teams to implement change to improve patients' and families' outcomes. Ongoing energy should focus on how to improve communication among all members of the team and ensure their support.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".