Providing care to people on social assistance: how dentists in Montreal, Canada, respond to organisational, biomedical, and financial challenges
Bibliographic record
Abstract
BACKGROUND: Dentists report facing difficulties and experiencing frustrations with people on social assistance, one of the social groups with the most dental needs. Scientists ignore how they deal with these difficulties and whether they are able to overcome them. Our objective was to understand how dentists deal with critical issues encountered with people on social assistance. METHODS: We conducted in-depth, semi-structured interviews with 33 dentists practicing in Montreal, Canada. The interview guides included questions on dentists' experiences with people on social assistance and potential strategies developed for this group of people. Analyses consisted of interview debriefing, transcript coding, and data interpretation. RESULTS: Dentists described strategies to resolve three critical issues: missed appointments (organisational issue); difficulty in performing non-covered treatments (biomedical issue); and low government fees (financial issue). With respect to missed appointments, dentists developed strategies to maximise attendance, such as motivating their patients, and to minimise the impact of non-attendance, like booking two people at the same time. With respect to biomedical and financial issues, dentists did not find any satisfactory solutions and considered that it was the government's duty to resolve them. Overall, dentists seem reluctant to exclude people on social assistance but develop solutions that may discriminate against them. CONCLUSIONS: The efforts and failures experienced by dentists with people on social assistance should encourage us to rethink how dental services are provided and financed.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".