ORAL-HEALTH PROFESSIONALS AS A FIRST POINT OF CONTACT FOR ELDER ABUSE VICTIMS: A SCOPING REVIEW
Bibliographic record
Abstract
Oral-health professionals, which include dentists, orthodontists and dental hygienists, are often the first point of contact for older adults experiencing various types of elder mistreatment (such as abuse and neglect). The scheduled and routine nature of the visit often provides an opportunity to recognize any indicators of mistreatment over a longer period of time. A 2017 technical report completed on support services for victims of elder mistreatment highlighted that oral-health professionals are an underutilized ally in identifying and intervening with abused or neglected older adults. However, training in the identification of elder mistreatment by oral-health professionals as well as protocols for reporting are not well established. In addition, entities such as the Public Health Agency of Canada have begun changing the scope from “dental professionals” to “oral-health professionals” to capture the important role this range of front-line professionals plays. A scoping review was completed to gain an in-depth understanding of the role of oral-health professionals with respect to elder mistreatment, and as crucial resources in community settings. Ten peer-reviewed and grey literature databases were searched for empirical studies published after 2000. This synthesis review analyzes approaches that oral-health professionals may utilize to identify different types of elder mistreatment and ensure their client’s safety moving forward (i.e., duty to report). Findings suggest that oral-health professionals equipped with appropriate education, training and awareness can be key in early detection of elder mistreatment. This merits further research, policy and practice attention to the role of oral-health professionals intervening in cases of elder mistreatment.
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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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".