Quality of oral health services in residential care: towards an evaluation framework
Bibliographic record
Abstract
BACKGROUND: There is widespread neglect of oral healthcare, and uncertainty about how best to organise and evaluate the impact of oral health services in long-term care (LTC) facilities. Consequently, there is need for an evaluation framework to improve and account for the quality of oral healthcare in the facilities. OBJECTIVES: This paper: (i) identifies basic concepts of quality of care and evaluation in healthcare; (ii) reviews the methods used to evaluate the operation and effectiveness of oral healthcare in LTC facilities and (iii) recommends change to assure oral health-related quality and accountability for frail elders. METHOD: A literature review provided insights to the theoretical basis and practical applications for assessing the quality of healthcare relevant to oral healthcare for frail elders. RESULTS: Oral health-related programmes in LTC facilities could be improved by using a combination of quality assurance and health programme evaluation that: (i) engages everyone involved; (ii) seeks multiple attributes of quality; (iii) evaluates the structure, process or activities, and outcome of the oral health programme; (iv) uses formative and summative methods to provide both quantitative and qualitative evidence of care and (v) transfers new knowledge for appropriate consideration and action. CONCLUSIONS: This theoretical framework can be applied in dentistry in LTC to provide an assessment model specific to oral healthcare for frail elders in residential care.
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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 | 0.263 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| 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".