The Productive Ward Program™
Bibliographic record
Abstract
Aim To investigate the impact of the quality improvement program "Productive Ward - Releasing Time to Care™" using nurses' and midwives' reports of practice environment, burnout, quality of care, job outcomes, as well as workload, decision latitude, social capital, and engagement. Background Despite the requirement for health systems to improve quality and the proliferation of quality improvement programs designed for healthcare, the empirical evidence supporting large-scale quality improvement programs impacting patient satisfaction, staff engagement, and quality care remains sparse. Method A longitudinal study was performed in a large 600-bed acute care university hospital at two measurement intervals for nurse practice environment, burnout, and quality of care and job outcomes and three measurement intervals for workload, decision latitude, social capital, and engagement between June 2011 and November 2014. Results Positive results were identified in practice environment, decision latitude, and social capital. Less favorable results were identified in relation to perceived workload, emotional exhaustion. and vigor. Moreover, measures of quality of care and job satisfaction were reported less favorably. Conclusion This study highlights the need to further understand how to implement large-scale quality improvement programs so that they integrate with daily practices and promote "quality improvement" as "business as usual."
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".