Exploring the Impact of the Covid-19 Pandemic on Peer Mentor Self-Efficacy and Wellbeing
Bibliographic record
Abstract
With the increasing attention to wellbeing and mental health (especially during the COVID-19 pandemic) as antecedents of meeting postsecondary students’ academic, emotional, and social needs, there is a need for research to understand how students’ wellbeing can be promoted through peer mentoring. The lack of engagement caused by COVID-19 pandemic restrictions has greatly affected peer mentors’ ability to meet and interact with their mentees, leading to decreased feelings of self-efficacy and wellbeing in the mentoring role. Therefore, we saw a need for research on how mentors are attuned to the importance of their own self-efficacy and wellbeing as an essential grounding for their mentoring practices to wellbeing among those they serve. This chapter details an exploratory study that examined the peer mentors’ perceptions of their experiences in peer mentoring programs at two institutions of higher education in the state of Florida in relation to the impacts of the pandemic on students’ self-efficacy and wellbeing. This study can help with understanding the specific, contextualized factors conducive to flourishing peer mentoring in educational institutions during times of significant change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".