Oral health status of long‐term care residents in Canada: Results of a national cross‐sectional study
Bibliographic record
Abstract
OBJECTIVE: To describe the oral health and oral prosthetic status of long-term care residents in four Canadian provinces. BACKGROUND: Oral health can have significant impact on the health and quality of life of older adults. Seniors in long-term care are highly dependent on care staff for basic activities of daily living and are at risk for poor oral health. MATERIALS AND METHODS: Five hundred and fifty-nine randomly selected residents were examined from thirty-two long-term care homes in Alberta, Manitoba, Ontario and New Brunswick, Canada. Four experienced registered dental hygienists, one in each province, completed a standardised oral health examination with each participant, examining lip health, breath odour, saliva appearance, natural teeth count, gingival inflammation, tooth and jaw pain, denture status, mucosal status and oral health abnormalities. RESULTS: Of the examined residents, 57.6% were dentate, with an average of 16.4 (SD = 8.0) teeth. Most dentate residents had moderate or severe inflammation on at least one tooth (79.6%). Sixty per cent of residents wore dentures, and 43.2% of edentulous residents had poor hygiene of their dentures. Nine per cent of residents required urgent dental treatment for oral health problems such as broken teeth, infection, severe decay and ulcers. CONCLUSION: This study provides an estimate of the prevalence of oral health problems in residents living in long-term care homes across Canada and indicates that improvement in oral health care is needed. Future work on development strategies aimed at optimising oral health for long-term care residents is required.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".