Examining Differences in Oral Health Status Based on Assessments Conducted by Long-Term Care Staff Versus a Dentist
Bibliographic record
Abstract
Long-term care demand is increasing as Canada’s population ages and experiences a growing prevalence of dementia. Long-term care residents are highly dependent on health care providers for ongoing assessment and care due to complex cognitive and other medical conditions. One aspect of health requiring particular attention is the oral cavity. The oral health status of older adult long-term care residents is generally poor and access to dental professionals is limited. Residents require regular oral assessments by health care providers to identify unmet oral health needs which may be detrimental to residents’ oral health, systemic health and psychosocial well-being. The only assessment of oral health considered compulsory in Alberta’s long-term care facilities is a component of the Resident Assessment Instrument Minimum Data Set (RAI-MDS 2.0). The present study describes the oral health conditions in a sample of older adult (65+) LTC residents taken from a retrospective chart review from 2009-2012. Each resident had received two independent oral assessments: one by a dentist and another by long-term care staff. Oral health data were extracted from RAI-MDS 2.0 records and were then compared with oral health assessments completed by a dentist on the same resident. Additionally, differences between oral health conditions recorded by a dentist and cognitive impairment using the RAI-MDS 2.0 Cognitive Performance Scale were explored. Oral health issues were frequently documented during the dentist’s oral assessment. Although the percentage of residents with oral health conditions identified by a dentist were generally comparable to other Canadian studies, some notable differences emerged from our study. These discrepancies may be related to varying definitions of oral health items, assessment methods used, and inclusion of residents with cognitive impairment. The findings highlight the challenges and complexities involved when assessing oral health of LTC residents who are frequently affected by cognitive impairment. In contrast to the dentist’s oral assessment, the RAI-MDS 2.0 oral assessments rarely identified unhealthy or problematic conditions. Comparisons between the RAI-MDS 2.0 oral health items and the oral health assessments recorded by a dentist on the same resident displayed low rates of agreement. These findings suggest that the RAI-MDS 2.0 oral health assessment process may not be meeting the oral health needs of older adults in LTC. Comparisons of residents who were cognitively intact to those with cognitive impairment revealed significant differences in the proportions of residents with certain oral health conditions. The differences between residents with and without cognitive impairment highlights the need for research specifically capturing the oral health status of cognitively impaired individuals. The cognitively impaired population who are frequently excluded from research samples appear to have unique oral health needs and challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".