Motivation of paid peer mentors and unpaid peer helpers in higher education
Bibliographic record
Abstract
While considerable research supports the use of peer mentoring to improve academic performance and decrease student attrition, few studies have examined the motives of peer mentors to take on this role and less clear are distinctions in peer mentor motivation in paid versus unpaid settings. Using semi-structured interviews, this study explored the motivations of student peer mentors in voluntary and paid peer mentoring services at the University of Ottawa, Canada. The findings showed that both paid and unpaid mentors reported being motivated by self-oriented reasons, such as learning about themselves and fulfillment, but that paid mentors were primarily motivated by generativity, or the desire to help young people, while volunteer peer helpers reported being highly motivated to fulfill social needs. This research helps shed light on the impact of payment on motivation to perform the mentoring function and on the communication strategies which may be used to attract student mentors to this position.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".