Understanding Veterinary Students' Use of and Attitudes toward the Social Networking Site, Facebook, to Assist in Developing Curricula to Address Online Professionalism
Bibliographic record
Abstract
Social media is an increasingly common form of communication, with Facebook being the preferred social-networking site among post-secondary students. Numerous studies suggest post-secondary students practice high self-disclosure on Facebook. Research evaluating veterinary students' use of social media found a notable proportion of student-posted content deemed inappropriate. Lack of discretion in posting content can have significant repercussions for aspiring veterinary professionals, their college of study, and the veterinary profession they represent. Veterinarians-in-training at three veterinary colleges across Canada were surveyed to explore their use of and attitude toward the social networking site, Facebook. Students were invited to complete an online survey with questions relating to their knowledge of privacy in relation to using Facebook, their views on the acceptability of posting certain types of information, and their level of professional accountability online. Linear regression modeling was used to further examine factors related to veterinary students' disclosure of personal information on Facebook. Need for popularity (p<.01) and awareness of consequences (p<.001) were found to be positively and negatively associated, respectively, with students' personal disclosure of information on Facebook. Understanding veterinary students' use of and attitudes toward social media, such as Facebook, reveals a need, and provides a basis, for developing educational programs to address online professionalism. Educators and administrators at veterinary schools may use this information to assist in developing veterinary curricula that addresses the escalating issue of online professionalism.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".