Continuing dental care for Highlands elderly: Current practice and attitudes of dental practitioners and home supervisors
Bibliographic record
Abstract
OBJECTIVE: To investigate current practice and attitudes of Highland dentists and home supervisors to continued dental care of elderly residents. METHODS: A cross-sectional questionnaire was designed to survey the current practice and attitudes of Highland dentists and residential care supervisors in their provision of dental care for the elderly at home and in long stay accommodation. RESULTS: The response rate was 94% of dentists and 79% of homes. Despite 86% of dentists providing domiciliary care and 93% of homes transport to a surgery, no more than a quarter of residents had had contact with a dentist in the previous year. The distribution of residents varied with dependant individuals living in nursing units and the least dependant in residential homes. Only 1% of all residents were totally bed bound. Domiciliary patients were less likely to receive continuing care compared with those seen in a surgery and 75% of homes had to initiate dental care. In terms of patient referral, the majority of GDPs would refer uncooperative patients, salaried dentists would refer those with complex medical histories and community dentists would refer those requiring complex treatments. A dental assessment was undertaken in 46% of homes and 81% of these kept a record of dental care. CONCLUSION: This study highlights the need for a co-ordinated, seamless continuing dental care service, tailored to the actual needs of the elderly individuals it is designed to serve, particularly in a remote and rural area.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".