Bibliographic record
Abstract
BACKGROUND: Social media have emerged as valuable tools for dentists to connect with patients; however, their misuse may adversely affect the patient-dentist relationship. Given the trend toward commercialized, cosmetically oriented values within the general population, this study examined how dentists use the LinkedIn social network to promote themselves as cosmetic dentists. It also assessed whether dentists' social media practices align with the professional and ethical obligations of dentistry's social contract, given that cosmetic dentistry is not a recognized specialty. METHODS: A cross-sectional, convenience sampling method was used to retrieve LinkedIn profiles of dentists from 6 Canadian provinces: British Columbia, Ontario, New Brunswick, Nova Scotia, Prince Edward Island, and Newfoundland and Labrador. Profiles were screened for the term "cosmetic" in the headline, position title and/or company name fields to determine whether dentists promoted themselves as cosmetic dentists or appropriately represented themselves as dentists with skills in cosmetic dentistry. RESULTS: A total of 896 dentists had LinkedIn profiles, of whom 612 were registered with their respective dental regulatory authorities and were eligible for screening, after which 527 profiles were subjected to content analysis. Overall, 469 (89%) of the 527 profiles included in the content analysis did not misrepresent their credentials. Ontario dentists had the highest frequency of misrepresentation, followed by British Columbia and Nova Scotia; for all 3 provinces, misrepresentation was most prevalent in LinkedIn headlines. Profiles of dentists in the other 3 Atlantic provinces (New Brunswick, Newfoundland and Labrador, and Prince Edward Island) had no mentions of the term "cosmetic." No significant relationship was found between the province of practice and the distribution of misrepresentation across the different sections of the LinkedIn profile (p = 0.84). CONCLUSION: These findings offer insight into the potential of social media to jeopardize dentistry's social contract and highlight the importance of professionalism as dentistry evolves.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.802 | 0.725 |
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".