Understanding Mentoring Relationships during and after COVID-19 Restrictions from the Perspective of Mentors: A Community-Engaged Participatory Approach
Bibliographic record
Abstract
Amidst the unprecedented challenges and uncertainty of the COVID-19 pandemic, youth and young adults have experienced a great deal of stress and challenges. Youth mentorship has been an important resource for many youths and has been shown to be a protective factor against such troubling times. However, mentors’ experiences during the pandemic and its impact on their ability to support youth remains unclear. By fostering resilience and positive outcomes in youth through mentorship, these efforts contribute to the overall well-being and empowerment of youth. This study aimed to understand the impact of COVID-19 on mentorships and mentor experiences, barriers and facilitators of mentorship and virtual mentorship, and the impact of inequity, compatibility, and diversity on mentoring relationships. In partnership with Big Brothers Big Sisters Canada, this qualitative study analyzed 20 mentors’ perspectives and experiences within these relationships and how they navigated the pandemic personally and while supporting their young mentee. Five major themes were constructed based on these interviews including; how mentors and mentees maintained their relationships despite the challenges and changing circumstances of the pandemic, personal benefits of mentorship, technological limitations and disparities in access to technology that made virtual mentorship less preferable, how support from those outside of the mentorship including parents and the mentorship program can impact the relationship, and the crucial role of compatibility and cultural discussions in mentorship. These findings have important implications for mentoring organizations including guiding the development of adaptive programs and policies to better support mentors and mentees in navigating challenging circumstances.
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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 | 0.040 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".