How accurate is the assessment of certified nursing assistants on resident's oral self‐care function in three North Carolina assisted‐living facilities?
Bibliographic record
Abstract
AIMS: To examine the association between the assessments of certified nursing assistants (CNAs) on resident's oral self-care function and resident's oral hygiene outcomes in three North Carolina assisted-living (AL) facilities. METHODS AND RESULTS: Sixty-five dentate AL residents were included in this secondary analysis. CNAs were asked to rate the AL residents' overall oral self-care function using a 6-point Likert scale. Their assessments were then compared with the objective, performance-based Dental Activities Test and the oral hygiene and gingival health measures. The analysis showed that nearly 90% of the participants presented with at least one untreated decayed or broken tooth. On average, nearly two-thirds of the surfaces were covered by soft deposits (Debris Index = 1.83, SD = 0.60). Generalized mild to moderate inflammation (Gingival Index = 1.51, SD = 0.53) commonly presented on residents' gingiva. CNAs were able to accurately identify the residents with substantial impairment in oral self-care function, yet they tended to overestimate the oral self-care function of residents with mild to moderate impairment. CNAs assessments were also not significantly correlated with residents' oral hygiene measures. CONCLUSION: CNAs failed to identify and provide assistance to AL residents with impaired oral self-care function, contributing to poor oral oral hygiene in these vulnerable individuals.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".