Reputation Management on Facebook: Awareness Is Key to Protecting Yourself, Your Practice, and the Veterinary Profession
Bibliographic record
Abstract
From the Social media use by health professionals occurs in a digital environment where etiquette has yet to be solidly defined. The objectives of this study were to explore veterinarians' personal use of Facebook, knowledge of privacy settings, and factors related to sharing personal information online. All American Animal Hospital Association member veterinarians with a valid e-mail address (9469) were invited to complete an online survey about Facebook (e.g., time spent on Facebook, awareness of consequences, types of information posted). Questions assessing personality dimensions including trust, popularity, self-esteem and professional identity were included. The response rate was 17% (1594 of 9469); 72% of respondents (1148 of 1594) had a personal Facebook profile. Veterinarians were more likely to share information on Facebook than they would in general. Trust, need for popularity, and more time spent on Facebook predicted more disclosure of personal information on Facebook. Awareness of consequences and increased veterinary experience predicted lesser disclosure. As veterinary practices use Facebook to improve client services, they need also to manage risks associated with online disclosure by staff. Raising awareness of reputation management and consequences of posting certain types of information to Facebook is integral to protecting the individual, the practice, and the veterinary 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 | 0.003 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".