Action Planning for Daily Mouth Care in Long-Term Care: The Brushing Up on Mouth Care Project
Bibliographic record
Abstract
Research focusing on the introduction of daily mouth care programs for dependent older adults in long-term care has met with limited success. There is a need for greater awareness about the importance of oral health, more education for those providing oral care, and organizational structures that provide policy and administrative support for daily mouth care. The purpose of this paper is to describe the establishment of an oral care action plan for long-term care using an interdisciplinary collaborative approach. Methods. Elements of a program planning cycle that includes assessment, planning, implementation, and evaluation guided this work and are described in this paper. Findings associated with assessment and planning are detailed. Assessment involved exploration of internal and external factors influencing oral care in long-term care and included document review, focus groups and one-on-one interviews with end-users. The planning phase brought care providers, stakeholders, and researchers together to design a set of actions to integrate oral care into the organizational policy and practice of the research settings. Findings. The establishment of a meaningful and productive collaboration was beneficial for developing realistic goals, understanding context and institutional culture, creating actions suitable and applicable for end-users, and laying a foundation for broader networking with relevant stakeholders and health policy makers.
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".