Provision of Government-funded and Pro Bono Dental Care: Are There Gender Differences?
Bibliographic record
Abstract
BACKGROUND: Government-funded and pro bono dental care are important to populations with limited means. At the same time, dentistry is experiencing a gender shift in the practising profession. As a result, we aimed to determine the factors associated with the provision of government-funded and pro bono dental care and whether there are gender differences. METHODS: We conducted a secondary data analysis of the results of a 2012 survey of a representative sample of Ontario dentists. Descriptive, bivariate and multivariable analyses were carried out. RESULTS: The 867 survey respondents represented a 28.9% response rate. On average, Ontario dentists reported that 15.7% of their practice consisted of government-funded patients and they provided $2242 worth of pro bono care monthly. Male and female dentists reported similar levels of both (p > 0.05). Being a practice owner and having more pediatric patients influenced levels of government-funded patients. Being internationally trained, of European ethnicity, single, and income status affected levels of monthly pro bono care. Gender-stratified analysis revealed that, among female dentists, household responsibilities was a unique factor associated with the proportion of government-funded patients, as was international training, personal income and ethnic origin for levels of pro bono care. CONCLUSION: Overall, male and female dentists are similar in the provision of government-funded and pro bono care, but various factors influence levels of each in both groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".