An Assessment of Oral Health Training Among Geriatric Fellowship Programs: A National Survey
Bibliographic record
Abstract
Oral health (OH) has profound effects on the overall health of elderly people. While oral disease is prevalent in the geriatric population and access to care is a major issue, it is unclear the extent of OH training among US geriatric fellowship programs. A 19-item electronic survey was sent to all 148 accredited geriatric fellowship training programs via the Association of Directors of Geriatric Medicine. Directors were asked about hours of trainings, barriers, and evaluation of trainees among other topics. Univariate and bivariate analyses were performed. Seventy-five directors completed the survey (51% response rate). Sixty-three percent (46/73) report their fellows receive 1 to 2 hours of OH instruction (ie, lectures, workshops) during their training. Almost a quarter (23%; 17/73) reported 0 hours of OH content. Only 17% (13/75) have clinical experiences in a dental setting. Barriers to more OH education include competing priorities or lack of time (57%; 43/75), lack of faculty expertise (55%; 41/75), and no clear geriatric national educational competencies (44%; 33/75). Programs with an OH champion or dental school/residency affiliation had more hours of OH instruction. Geriatric fellowships appear to need more OH training, which could be achieved by creating OH champions and connecting fellowships with dental schools/residencies. Barriers could be overcome by exposing fellowships to existing resources and creating national competencies. J Am Geriatr Soc 67:1079-1084, 2019.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".