The effect of provincial legislation on oral health in continuing care facilities.
Bibliographic record
Abstract
OBJECTIVE: In Alberta, a provincial daily oral hygiene policy for continuing care facilities (CCF) was approved in 2018, and a mouth care training program was implemented in 2015. These initiatives require CCF to provide residents with twice-daily oral hygiene assistance and staff training. This study aimed to evaluate the provincial implementation scope, compliance rates, perceived impact on residents, and areas for improvement. METHOD AND MATERIALS: A web-based survey was distributed to CCF managers across Alberta, with 77 responses representing 11,653 residents. RESULTS: Overall, 66.2% of CCF managers had implemented the oral hygiene policy, and 50.6% implemented the mouth care training program. Managers implementing these policies showed a 14.3% increase in twice-daily oral hygiene provision, with 31.2% reporting improved oral cleanliness. Notably, 22.1% indicated improved resident quality of life, and 55.2% observed increased staff oral health knowledge. However, reliance on family/caregivers for oral hygiene products (79.2%) and poor attendance at external dental appointments (67.6%) were significant barriers reported by managers. CONCLUSION: While policy implementation has positively impacted residents and staff, gaps remain in resource availability and professional support. Future policies should focus on enhancing access to oral health professionals and providing in-house dental resources.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".