Acceptability of the Expert Standard for Oral Health Care in Older Adult Patients Among Nursing Staff in German Hospitals and Care Facilities: Protocol for a Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The aging population and increasing prevalence of natural teeth among older adults have escalated the demand for oral health care, especially in nursing settings. Impaired oral health in older individuals is closely linked to systemic conditions such as diabetes and cardiovascular diseases. The expert standard "promoting oral health in nursing" was developed in Germany to enhance the quality of oral care and address future challenges in geriatric nursing. It comprises a series of recommended interventions targeting oral health promotion in nursing care. However, significant barriers, including high patient-to-nurse ratios and staff shortages, often result in missed or rationed nursing care, limiting the feasibility and implementation of such interventions. Evaluating the acceptability of this standard is critical to its successful integration into routine nursing practice. OBJECTIVE: We aim to assess the acceptability of the expert standard among nursing staff providing care for older individuals, identify factors influencing its adoption, and examine the relationship between nursing competence, care rationed or missed (CROM), and the standard's acceptability. METHODS: This quantitative cross-sectional study will collect data from nursing staff in 25 hospitals and long-term care facilities in North Rhine-Westphalia, Germany, using standardized survey instruments. Based on the template of the generic theoretical framework of acceptability, a questionnaire to measure the acceptability of interventions across 7 domains was created. Oral health knowledge will be assessed using the Oral Health Literacy Profile and competence in mouth care using the questionnaire developed by the DNQP (Deutsches Netzwerk für Qualitätsentwicklung in der Pflege [German network for Quality Development in Nursing]). Barriers to implementation will be evaluated according to the acute care nurses' questionnaire on oral hygiene and CROM using the oral care-related question from the Basel Extent of Rationing of Nursing Care instrument. Statistical analyses consist of first calculating the mean acceptability with a 95% CI for each recommended intervention of the expert standard. Second, repeated measures ANOVA is used to examine mean differences in acceptability between these interventions. Third, linear regression analyses are used to test the impact of nursing competence on acceptability and lastly chi-square tests of independence are used to compare CROM with already published rates in German-speaking countries. RESULTS: The results are anticipated to provide insights into the acceptability of the expert standard and its determinants, including nursing competence and perceived barriers. Data collection will commence in June 2025 and is expected to be completed by October 2025. CONCLUSIONS: This study evaluates the acceptability of the expert standard for oral health in nursing. The findings will support evidence-based strategies to enhance feasibility, reduce CROM prevalence, and improve oral health in older adults. By focusing on acceptability as a prerequisite for implementation, this study emphasizes the need to align interventions with the realities of nursing care to achieve effective outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/72528.
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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 | 0.032 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".