Exploring the Impact and Implications of Oral Health Interventions among Older Adults: A Scoping Review
Bibliographic record
Abstract
Abstract Aim: This scoping review synthesizes current evidence on the impact of oral health interventions–including education, promotion, and preventive care–on oral health outcomes in older adults. Materials and Methods: Following Arksey and O’Malley’s methodological framework and reported per PRISMA-ScR guidelines, a comprehensive search was conducted in PubMed, Embase, Scopus, Web of Science, and Google Scholar for studies published from January 2004 to March 2024. Eligible studies included observational or interventional designs focused on oral health interventions among adults aged 60 years and above. Data extraction was independently performed by multiple reviewers, and qualitative synthesis was conducted using ATLAS.ti 24.1.0. Methodological quality was assessed using Crowe’s Critical Appraisal Tool. Results: Of 2014 articles initially identified, six studies were included–four randomized controlled trials and two non-randomized interventions–spanning sample sizes of 67 to 306 and intervention durations from 8 weeks to 2 years. Interventions included oral and denture hygiene instruction, use of chlorhexidine and fluoride-based products, behavioral models (e.g., Health Belief Model), and caregiver-delivered oral care programs. Analysis revealed improvements in plaque and gingival scores, oral hygiene behaviors, and oral health-related quality of life. Key factors influencing intervention outcomes included cognitive status, manual dexterity, and frequency of caregiver involvement. Conclusion: Oral health interventions, even when basic, can lead to measurable behavioral and clinical improvements among older adults. Their success is influenced by delivery method, duration, and participant capacity. These findings underscore the need for scalable, personalized, and preventive strategies in geriatric oral healthcare and support integration into existing long-term care and public health frameworks.
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 | 0.036 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".