Continuous quality improvement through team supervision supported by continuous self-monitoring of work and systematic patient feedback
Bibliographic record
Abstract
BACKGROUND: Evaluation of clinical supervision (CS) and exploration of its effects on the quality of care is a timely topic for research. The current emphasis in nursing is shifting towards continuous quality improvement (CQI), and the integration of this with CS seems to be an interesting challenge. So far the studies have relied mainly on supervisees' self-report data and patients have rarely been involved in research. However, the perspective of CQI requires that patients are involved in the quality improving efforts. AIM OF THE STUDY: The aim of this study is to describe how CQI was implemented through team supervision and supported by continuous self-monitoring of work and systematic patient feedback. METHODS: The team supervision intervention was organized on five wards between 1995 and 1998. The methods of statistical process control and control charts were applied in the study as part of the intervention. FINDINGS: Improvements in both patient satisfaction and the staff's self-monitoring of work were evidenced. A slow and minor upward trend was detected in the control charts and the variation decreased in the assessments. The patients' high and the staff's critical ratings drew nearer towards the end of the study. However, significant differences were found between the wards and not all wards showed improvements. Staff found it difficult to discern the effects of continuous patient satisfaction feedback and self-monitoring. CONCLUSIONS: The findings of the study show that CQI integrated with team supervision improves patient satisfaction and the overall quality of care.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".