A scoping review and research synthesis on financing and regulating oral care in long‐term care facilities
Bibliographic record
Abstract
BACKGROUND: Oral health care for frail elders is grossly inadequate almost everywhere, and our knowledge of regulating and financing oral care in this context is unclear. OBJECTIVE: This scoping study examined and summarised the published literature available and the gaps in knowledge about regulating and financing oral care in long-term care (LTC) facilities. METHODS: We limited the electronic search to reports on regulating and financing oral care, including reports, commentaries, reviews and policy statements on financing and regulating oral health-related services. RESULTS: The broad electronic search identified 1168 citations, which produced 42 references, including 26 pieces of grey literature for a total of 68 papers. Specific information was found on public and private funding of care and on difficulties regulating care because of professional segregation, difficulties assessing need for care, uncertainty on appropriateness of treatments and issues around scope of professional practice. A wide range of information along with 19 implications and 18 specific gaps in knowledge emerged relevant to financing and regulating oral healthcare services in LTC facilities. CONCLUSIONS: Effort has been given to enhancing oral care for frail elders, but there is little agreement on how the care should be regulated or financed within the LTC sector.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".