Dentists' Views on Providing Care for Residents of Long-Term Care Facilities.
Bibliographic record
Abstract
INTRODUCTION: People living in long-term care (LTC) facilities face many oral health challenges, often complicated by their medical conditions, use of medications and limited access to oral health care. OBJECTIVE: To determine Manitoba dentists' perspectives on the oral health of LTC residents and to identify the types of barriers and factors that prevent and enable them to provide care to these residents. METHODS: Manitoba general dentists were surveyed about their history of providing care and their views on the provision of care to LTC residents. Descriptive statistics, bivariate analysis and logistic regression analysis were carried out. RESULTS: Surveys were emailed to 575 dentists, with a response rate of 52.5%. Most respondents were male (62.8%), graduates of the University of Manitoba (85.0%), working in private practice (89.8%) and located in Winnipeg (72.4%). Overall, only 26.2% currently treat LTC residents. A predominant number of respondents identified having a busy private practice (60.0%), lack of an invitation to provide dental care (53.0%) and lack of proper dental equipment (42.6%) as barriers preventing them from seeing LTC residents. Receiving an invitation to provide treatment, professional obligation and past or current family or patients residing in LTC were the most common reasons why dentists began treating LTC residents. CONCLUSION: Most responding dentists believe that daily mouth care for LTC residents is not a priority for staff, and only a minority of dentists currently provide care to this population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".