Do dentists' views on professionalism include moral inclusiveness?
Bibliographic record
Abstract
To successfully tackle inequity in oral health and access to dental care, it is important to understand how dentists perceive their duty to care for others. The scope of concern for others can be studied through the concepts of moral inclusion, moral community and moral inclusiveness. Moral inclusion is the application of moral values, rules and considerations of fairness towards others. Moral community is the group(s) of people to whom one applies moral inclusion. Moral inclusiveness is the breadth of one’s moral community, and may range from narrow, if one is only concerned for family and friends, to broad, if one is concerned for all of society. To assess these parameters among dentists, a mail survey was sent to 3,201 randomly selected dentists in the Province of Ontario, Canada’s most populous province and the largest dental care market. Moral inclusiveness was measured using a ‘moral community score’, composed of the sum of the responses to a question about the dentist’s duty to care for different social groups. Dentists’ views on moral inclusiveness were measured by their agreement with Likert-type scale questions. While the majority of dentists did agree with morally inclusive views, they also showed bias against specific patient groups. Further, dentists’ demographic and practice characteristics and business considerations were related to having a broad moral community. We contend that moral inclusiveness should be an integral part of dentists’ professionalism, and that understanding the moral inclusiveness of dentists is important to reducing oral health-related inequity.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".