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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".