Bridging the Poverty Gap in Dental Education: How Can People Living in Poverty Help Us?
Bibliographic record
Abstract
Dental education on specific knowledge and intervention approaches for working with people living on welfare is crucial to the therapeutic success of the relationships dental professionals establish with this clientele. Despite growing attention to the importance of cultural competence and communication skills training in dentistry, very few initiatives have been documented in relation to serving low-income populations. Following discussions at a 2006 Montreal-based colloquium on access to dental care, academics, dental association administrators, and public health agency and antipoverty coalition representatives began collaborating to develop innovative pedagogy designed to increase providers' competence in interacting with their underprivileged patients. The group's first round of workshops (November 2006-October 2007) resulted in the creation of an original video-based tool containing testimonies from six individuals living currently or formerly on welfare. The videotaped interview data represent their perceptions and experiences regarding their oral health, dental care service provision, and poverty in general. This article describes the participative methods, the content of the resulting DVD, and the implications of the "Listening to Each Other" program, a collaborative knowledge translation approach for improving interaction between underprivileged people and dental care providers.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".