Examining the impact of a university mentorship program on student outcomes
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the outcomes of a formal university mentorship program that paired junior and senior (third and fourth year) undergraduate student mentees with mentors from industry. Specifically, the researchers examined the effects of mentorship on mentees’ psychological sense of community at the university, and job search self-efficacy (confidence). Design/methodology/approach The researchers used a mixed-methods design that incorporated survey data and qualitative data from interviews and focus groups. Where relevant, mentees were compared to a control group of students who did not participate in the mentorship program. Findings The results demonstrate that the mentees accrued several benefits from participating in the mentorship program. For mentees (but not the control group), job search self-efficacy increased over time as a result of participating in the program. Mentees valued receiving practical career-related support such as opportunities for networking, resume development and job interviewing skills. Mentees also gained a more realistic view about the workplace and their potential career options, and received important psychosocial support from their mentor. Originality/value Results suggest that junior and senior undergraduate student mentees gained professional and career-related benefits including increased job search self-efficacy from participation in a mentorship program that paired them with mentors from industry. In addition, the qualitative results indicate that mentees reported psychosocial benefits including an increased sense of connection to the university. Altogether, results indicate that undergraduate students experience positive outcomes from participating in mentorship programs designed to prepare them for the transition from university to the workplace.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".