The Impact of Quality Assurance Programming: A Comparison of Two Canadian Dental Hygienist Programs
Bibliographic record
Abstract
Quality assurance (QA) and continuing competence (CC) programs aim to ensure acceptable levels of health care provider competence, but it is unknown which program methods most successfully achieve this goal. The objectives of the study reported in this article were to compare two distinct QA/CC programs of Canadian dental hygienists and assess the impact of these two programs on practice behavior change, a proxy measure for quality. British Columbia (BC) and Ontario (ON) were compared because the former mandates continuing education (CE) time requirements. A two-group comparison survey design using a self-administered questionnaire was implemented in randomly selected samples from two jurisdictions. No statistical differences were found in total activity, change opportunities, or change implementation, but ON study subjects participated in significantly more activities that yielded change opportunities and more activities that generated appropriate change implementation, meaning positive and correct approaches to providing care, than BC dental hygienists. Both groups reported implementing change to a similarly high degree. The findings suggest that ON dental hygienists participated in more learning activities that had relevancy to their practice and learning needs than did BC subjects. The findings indicate that the QA program in ON may allow for greater efficiency in professional learning.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".