Dentists’ experience with low‐income patients benefiting from a public insurance program
Bibliographic record
Abstract
France has a system of public coverage that guarantees low-income earners full payment of basic dental health costs. In spite of this coverage and major needs for care, deprived populations have lower access to dental care. The aim of this qualitative study was to explore dentists' experience with low-income patients benefiting from the French universal healthcare coverage system. This study is based on 17 one-on-one semistructured interviews carried out with French private dentists. Dentists distinguished two categories of low-income patients: 'good patients', described as being regular attenders; and 'bad patients', whose main characteristic is irregular attendance. Dentists explained that they have difficulties in dealing with patients who do not keep their appointments. First, dentists feel that they fail in conducting their mission of being a care provider (therapeutic failure). The absence of the patient is also seen as a lack of recognition (relationship failure). Furthermore, dentists do not earn money when patients miss their appointments (financial failure). In this context, many dentists feel discouraged and powerless (personal failure). Moreover, dentists do not understand why patients renounce the dental-care opportunities offered under the system of public coverage (failure of the system). Dentists who repeatedly experience failures related to irregular attendance tend to adopt exclusion strategies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".