Dentists’ Practice Patterns and Intervention Activities Under Indicator Linkage Management System
Bibliographic record
Abstract
The Health Insurance Review and Assessment Service has implemented the Indicator Linkage Management System (ILMS), which is designed to increase quality of care in healthcare organizations. We analyzed the effects of intervention activities under ILMS on dentists’ practice patterns and explored the factors affect the practice patterns. The visit index and costliness index were used to measure practice patterns. We used a randomized control group pre-post study design. The intervention activities were applied during the second quarter in 2016. The indices in the first quarter in 2016 were compared to the fourth quarter. The total of 994 dental clinics in Seoul metropolitan city were selected as the study sample. We used t-test for the pre-post comparison and performed multivariate ordinary least squares regression analysis to determine the predictors of dentists’ practice patterns. Both indices decreased after the intervention activities were applied. The most significant predictors of practice patterns were the types and the cumulative number of intervention activities. The findings of the study show that intervention activities under ILMS leads to the changes in dentists’ practice patterns. Results have implications for efforts to influence practice patterns in dental clinics and administering the ILMS standards in healthcare organizations.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".