Partnerships to Improve Oral Hygiene Practices: Two Complementary Approaches
Bibliographic record
Abstract
The omission of oral care is linked to increased nurse workload and may contribute to serious patient infection and growing healthcare costs. Therefore, ineffective oral care comprises a significant patient safety issue across healthcare settings internationally. As studies have demonstrated a positive relationship between Nurs Leadersh (Tor Ont) and improved patient outcomes, it is imperative that leaders seek effective approaches to facilitate contextual exploration of barriers and facilitators for resolution of oral care delivery problems. One approach to improved processes of oral care is the creative engagement of front-line clinicians in the problems they confront in everyday practice. By drawing upon the role and process of facilitation, we outline two projects, located in Australia and Canada, that engaged front-line nurses, health leaders, and researchers as partners to identify a path to improved oral care delivery. In this paper, we summarize key learnings for nursing leaders about strategies to facilitate delivery of fundamental oral care. We found that facilitation, contextual knowledge and academic-clinician partnerships were essential to the detection and evaluation of oral care delivery problems and the identification of priorities for practice improvement. As collaboration is imperative for sustainable innovation, we summarize strategies of effective leadership for improving oral care delivery.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".