Integration of a dental hygienist into the interprofessional <scp>long‐term</scp> care team
Bibliographic record
Abstract
BACKGROUND: To address poor oral health of residents in long-term care homes (LTCH), this study explored the process of integrating an educational resource and a dental hygienist on the interprofessional care team. METHODS: This convergent mixed-methods study took place at a 472-bed LTCH in Toronto, Canada from February to August 2018. Nurses employed at the LTCH participated in the study. During the study period, a dental hygienist was integrated into an interprofessional LTCH team. Nurses completed an online eLearning module about using the Oral Health Assessment Tool (OHAT) when referring residents' oral health concerns to a. Pre/post knowledge quizzes, module feedback and satisfaction surveys were administered. A retrospective chart review examined OHAT use and compared nurse and dental hygienist oral health assessments. Two cycles of semi-structured interviews with five nurses explored experiences with the eLearning module, OHAT and integration of the dental hygienist into the team. RESULTS: Nurses scored well on the knowledge quizzes and reported comfort in using the OHAT to refer oral concerns to a dental hygienist; however, actual use was minimal. oral health issues were under-reported by nurses on the Resident Assessment Instrument-Minimum Data Set (RAI-MDS); the dental hygienist reported significantly more debris, teeth lost and carious teeth (all P < 0.0001). Qualitative analysis indicated that the nurses valued dental hygienist integration into the team. Using knowledge mobilisation practices, a new oral health referral tool was developed. CONCLUSIONS: This study highlights the feasibility and desirability of an oral health eLearning module, practical assessment tools and participation of a dental hygienist on the LTCH interprofessional care team.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".