Total quality management tools and techniques for improving service quality and client satisfaction in the healthcare environment: A qualitative systematic review
Bibliographic record
Abstract
This study aims to analyse the total quality management tools for improving service quality and client satisfaction in healthcare settings through a systematic qualitative review. Data was collected through the web of sciences (WOS), Scopus, EBSCO, PubMed, and Medline. Initially, we found 573 articles from all the sources, but after eliminating the non-relevant articles, only 24 usable articles were finalized. Furthermore, 12 articles were purely related to TQM, service quality, and client satisfaction. This study concludes that TQM practices and tools improve service quality and client satisfaction in healthcare organizations. This study provides excellent managerial and practical insights. Managers should implement the TQM tools to improve service quality and client satisfaction. This way, customer satisfaction is enhanced, and patient satisfaction is improved, leading to high operational and overall performance. This study also reveals a need for further studies to clarify the role of TQM tools on service quality and patient satisfaction.
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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 | 0.057 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".