Interventions supporting community nurses in the provision of Oral healthcare to people living at home: a scoping review
Bibliographic record
Abstract
BACKGROUND: Oral health is a critical issue for public health and poor oral health is associated with significant chronic health conditions and lower quality of life. There has been little focus on providing oral health care to people who receive care in their own homes, despite the high risk of poor oral health in older people. Nurses practicing in the community are well placed to deliver this care, but little is known about how to build this capability through education or training interventions. METHODS: A scoping review methodology was employed to find and review studies of oral health interventions involving populations of people receiving care in their own home or those nurses who deliver this care. The research question asked what previous research tells us about oral health interventions delivered by nurses in the community. Data was extracted for four areas: setting and type of intervention, patient outcomes, changes to nursing practice and implementation and process evaluations of interventions. RESULTS: Two thousand eighty papers were found from the searches, and only nine were ultimately deemed eligible for inclusion in the review. Included studies spanned community nursing for older people (n = 3) and health visiting or community nursing for children and infants (n = 6). Patient outcomes were generally positive, but this is based on a low level of evidence. Changes to practice including increased oral health care administered by nurses were found, but this required professional support to be sustainable. CONCLUSIONS: This review has found that there is a clear gap in the research around interventions designed to be used by community nurses to improve oral health care for people receiving care in their own homes. The results also suggest that any future intervention must make use of a participatory, co-design approach and consider the complex setting of nursing practice in the community and the barriers to delivering this care, such as time pressure and lack of prior experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".