Teaching Veterinary Professionalism in the Face(book) of Change
Bibliographic record
Abstract
Facebook has been identified as the preferred social networking site among postsecondary students. Repeated findings in the social networking literature have suggested that postsecondary students practice high personal self-disclosure on Facebook and tend not to use privacy settings that would limit public access. This study identified and reviewed Facebook profiles for 805 veterinarians-in-training enrolled at four veterinary colleges across Canada. Of these, 265 (32.9%) were categorized as having low exposure, 286 (35.5%) were categorized as having medium exposure, and 254 (31.6%) were categorized as having high exposure of information. Content analysis on a sub-sample (n=80) of the high-exposure profiles revealed publicly available unprofessional content, including indications of substance use and abuse, obscene comments, and breaches of client confidentiality. Regression analysis revealed that an increasing number of years to graduation and having a publicly visible wall were both positively associated with having a high-exposure profile. Given the rapid uptake of social media in recent years, veterinary educators should be aware of and begin to educate students on the associated risks and repercussions of blurring one's private life and one's emerging professional identity through personal online disclosures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".