Perceived professional roles and implications for clinical decision‐making
Bibliographic record
Abstract
OBJECTIVES: This study aims to (a) investigate the relationship between dentists' perceived professional role (PPR), defined as the belief that they are health care professionals versus business people, and treatment intensity, determined by the aggressiveness of clinical approaches, such as in number or scope, and (b) identify the demographic and practice characteristics that have a relationship to PPR. METHODS: A 46-item survey with questions on dentists' demographic and professional characteristics was mailed to a random sample of 3,201 general dentists in Ontario, Canada. PPR was measured by visual analog scale and by Likert-type scale questions, which have been validated in the literature in terms of their ability to measure PPR. Treatment intensity was measured by a set of case scenarios. Univariate, bivariate, and multivariable analyses were performed. RESULTS: One-thousand and seventy-five dentists returned usable surveys (33.6% response rate). When using the two methods to measure PPR, visual analog scale and Likert-type scale questions, dentists who identified as business people tended to have a higher treatment intensity compared to those who identified as health care professionals (p < 0.1 and p < 0.05, respectively). In multivariable logistic regression, years of practice, number of technologies used in a practice, and perceiving other dentists as competitors rather than colleagues were significant predictors of identifying as a business person. CONCLUSIONS: Dentists' PPRs had a significant relationship to the aggressiveness of treatment decisions. Demographic and practice characteristics also had significant relationships to PPR. These findings may have implications for public trust and dentistry's status as a health care profession.
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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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".