Bibliographic record
Abstract
Quality of health care delivery is a growing concern globally given current budget restraints and increasing demands on health care providers. The variety of quality assurance and quality management activities equals the numerous ways health care practitioners of all genres provide health care. Dental hygienists around the world must be knowledgeable about quality assurance and management in health care as it is a significant factor in the evolution of the dental hygiene profession and the quality of oral health care provided by dental hygienists. The objective of this research was to conduct a literature review on quality assurance and quality management. A MEDLINE search from 1966 to 2002 was conducted. The search resulted in approximately 145 articles. Additional references from works generated by the search were also obtained. The literature revealed information on the background and history of quality assurance and quality management. Much of the literature was devoted to discussions of the validity, reliability and effectiveness of most prominent quality management activities being utilised in health care today. The investigation revealed numerous issues and barriers surrounding quality management. This article concludes with suggestions for future directions of quality assurance and quality management.
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 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 | 0.031 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".