How health professionals perceive and experience treating people on social assistance: a qualitative study among dentists in Montreal, Canada
Bibliographic record
Abstract
BACKGROUND: In Canada, the prevalence of oral diseases is very high among people on social assistance. Despite great need for dental treatment, many are reluctant to consult dental professionals, arguing that dentists do not welcome or value poor patients. The objective of this research was thus to better understand how dentists perceived and experienced treating people on social assistance. METHODS: This descriptive qualitative research was based on in-depth semi-structured interviews with 33 dentists practicing in Montreal, Canada. Generally organized in dentists' offices, the interviews lasted 60 to 120 minutes; they were digitally recorded and later transcribed verbatim. The interview transcripts were coded with NVivo software, and data was displayed in analytic matrices. Three members of the research team interpreted the data displayed and wrote the results of this study. RESULTS: Dentists express high levels of frustration with people on social assistance as a consequence of negative experiences that fall into 3 categories: 1) Organizational issues (people on social assistance ostensibly make the organization of appointments and scheduling difficult); 2) Biomedical issues (dentists feel unable to provide them with adequate treatment and fail to improve their oral health); 3) Financial issues (they are not lucrative patients). To explain their stance, dentists blame people on social assistance for neglecting themselves, and the health care system for not providing adequate coverage and fees. Despite dentists' willingness to treat all members of society, an accumulation of frustration leads to feelings of powerlessness and discouragement. CONCLUSIONS: The current situation is unacceptable; we urge public health planners and governmental health agencies to ally themselves with the dental profession in order to implement concrete solutions.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".