How Do Dentists Perceive Poverty and People on Social Assistance? A Qualitative Study Conducted in Montreal, Canada
Bibliographic record
Abstract
Despite significant needs, people on social assistance are sometimes reluctant to consult dentists because of previous negative experience and communication barriers. They feel poorly understood by oral health professionals and sometimes complain of being stigmatized. It is thus important to know how dentists perceive poverty and this group of patients. The aim of this study was to understand how dentists perceive poverty and people on social assistance. To investigate this largely unexplored question, a qualitative study was conducted based on in-depth interviews with thirty-three dentists practicing in Montreal, Canada. Interviews were audiotaped and transcribed for qualitative analysis. The study revealed two perspectives on poverty: 1) the individualistic-deficit perspective and 2) the socio-lifecourse perspective. In the individualistic-deficit perspective, which predominated among these participants, dentists explained poverty by individual factors and emphasized individuals' negative attitudes toward work and lack of capabilities. Conversely, dentists with a socio-lifecourse perspective described poverty as a structural rather than an individual process. Acknowledging individuals' distress and powerlessness, these dentists expressed more empathy toward people on social assistance. The results suggest the individualistic-deficit perspective impedes the care relationship between dentists and poor patients as well as highlighting the need to better prepare dentists for addressing issues of poverty and social inequities in clinical practice.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".