Factors that Influence the Decision to Pursue an Internship: The Importance of Mentoring
Bibliographic record
Abstract
The purpose of the present study was (1) to determine if students from one veterinary school who participated in a mentoring/employment program with clinical faculty were more likely to pursue internship training than their peers and (2) to determine factors via survey that were influential to veterinary interns in making their decision to pursue post-graduate clinical training. Our hypothesis was that a mentoring relationship with clinical faculty was an important influence on the decision to participate in an internship. From 2006 to 2010, graduating students who participated in a mentoring/employment program with a clinical faculty member were 6.3 times more likely than non-participating students to pursue an internship. The majority of the participating students (90%) were initially hired/mentored as first- or second-year veterinary students. In the survey, interns ranked clinical faculty as having a greater influence than basic science faculty, private practice veterinarians, or house officers on their decision to pursue an internship; 82.8% reported that clinical faculty were most responsible for encouraging them to apply for an internship. Employment by their veterinary teaching hospital (41.5%) or directly by clinical faculty (26.2%) was commonly reported. Most interns (37%) decided to pursue an internship during their fourth year of veterinary school, 29.2% decided during their first year, and 15.3% decided in their second year. These results suggest that clinical faculty play a key role in a student's decision to pursue an internship and that it might be valuable to inform students about internships early in the veterinary curriculum.
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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 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".