An International Working Definition for Quality of Oral Healthcare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To assess and improve the quality of oral healthcare, we must first agree on what constitutes good care. Currently there is no internationally accepted definition for quality of oral healthcare. Therefore, the purpose of the study was to establish a working definition for quality of oral healthcare that would help to advance further improvements in the field of quality improvement in oral healthcare. The development of the working definition included a 3-step approach: 1) literature screening; 2) expert-based compilation of an initial list of topics, leaning on the National Academy of Medicine framework for quality of care; and 3) a World Café with voting, which took place during the annual general meeting of the International Association for Dental Research in 2018. Following this approach, the collective intelligence of involved participants yielded a comprehensive list of items, prioritized by relevance. The resulting working definition comprises 7 domains—patient safety, effectiveness, efficiency, patient-centeredness, equitability, timeliness, access to care—and 30 items, which together characterize quality of oral healthcare. This aspirational working definition provides the potential to facilitate further conversations and activities aiming at quality improvement in oral healthcare. KNOWLEDGE TRANSFER STATEMENT: This special communication describes the development of a working definition for quality of oral healthcare. The findings of this study are intended to raise awareness of the relevance of quality improvement initiatives in oral healthcare. The working definition described here has the potential to facilitate further conversations and activities aiming at quality improvement in oral healthcare.
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.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it