Factors associated with regular dental attendance by aged adults: A systematic review
Bibliographic record
Abstract
OBJECTIVES: To determine factors influencing regular dental attendance in aged adults 65 and over according to Andersen's Behavioural Model. BACKGROUND: Regular attendance for dental visits is vital to improve and maintain oral health, quality of life and general well-being. Aged adults 65 years and older experience barriers to regular dental attendance, which in turn leads to an increased risk for oral diseases. MATERIALS AND METHODS: An electronic search was undertaken in April 2021 in Cochrane, Embase, Medline, Cinahl, Dentistry & Oral Science Source via EBSCOhost and Embase for papers on factors influencing the frequency of attendance by older people. Risk of bias was assessed according to the Newcastle-Ottawa Scale for cohort and case-control studies, and with modified version of this tool for cross-sectional studies. Frequency effect size was calculated for factors described in Andersen's Behavioural Model (predisposing, enabling and needs-related). RESULTS: Twenty-one studies were eligible for inclusion. Factors frequently investigated affecting regular dental attendance included: age, gender, education (predisposing); income, and social support (enabling); and remaining teeth, pain, perceived health (needs-related). Income was the only factors with a 100% positive association with regular dental attendance. CONCLUSIONS: This systematic review confirms the complex interconnectedness of several factors and dental attendance in older adults. A number of factors were identified which warrant further investigation to improve access to dental care to socio-economically vulnerable older populations.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".