Researching Applicants Online in the Veterinary Program Admissions Process: Perceptions, Practices, and Implications for Curricular Change
Bibliographic record
Abstract
As the use of social media websites continues to grow among adults 18-34 years old, it is necessary to examine the consequences of online disclosure to the veterinary admissions processes and to consider the effects on the professional integrity of veterinary schools and on the e-professionalism of DVM graduates. Prior research has shown that employers, across all fields, routinely use information from social media sites to make hiring decisions. In veterinary medicine, a little over one-third of private practitioners reported using online information in the selection of new associates. However, professional academic programs appear to use online information less frequently in the selection processes. The current study examines the behaviors and attitudes of veterinary medical admissions committees toward the use of applicants' online information and profiles in their recruitment and selection process. An online survey was distributed to Associate Deans for Academic Affairs at all AAVMC-affiliated schools of veterinary medicine. A total of 21 schools completed the survey. The results showed that most veterinary schools do not currently use online research in their admissions process; however, most admissions committee members feel that using online social networking information to investigate applicants is an acceptable use of technology. Previous research has suggested that the majority of veterinary student applicants view this as an invasion of their privacy. Given this discordance, future educational efforts should focus on helping veterinary students determine what type of information is appropriate for posting online and how to use privacy settings to control their sharing behaviors.
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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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".