Health care professionals’ perspectives on oral care for long‐term care residents: Nursing staff, speech–language pathologists and dental hygienists
Bibliographic record
Abstract
BACKGROUND: Oral health has been identified as a key factor in general health and systemic disease in long-term care populations. To optimise oral health of this population, it is important to understand the oral care perspectives held by health care professionals involved in oral care provision. OBJECTIVES: To explore perspectives regarding oral care held by nursing staff, speech-language pathologists (SLPs) and dental hygienists (DHs) in long-term care institutions and to understand how their perspectives impact activities and processes involved in the delivery of oral care. METHODS: A focus group methodology was utilised. Separate focus groups for each targeted profession were held. Transcribed data were analysed using constant comparative analysis. RESULTS: Daily oral health maintenance and monitoring was considered a role of nursing staff. SLPs and DHs have roles focusing on advocacy, education and supplemental care. Social factors motivate nursing staff to provide oral care, whereas factors related to the general health consequences of poor oral health underlined the motivations of SLPs and DHs. CONCLUSIONS: Education and training initiatives incorporating social aspects of oral health may be more effective for motivating nursing staff than approaches emphasising physical risk factors. Organisations can foster environments that support collaboration and communication amongst the members of multidisciplinary teams in order to promote oral health as a priority.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".