Youth Experiences in Virtual Mentorship Programs During COVID-19
Bibliographic record
Abstract
"Background. During the pandemic, estimates of depression and anxiety among youth doubled, and services were disrupted. Research shows mentorship to be protective of youth mental health, suggesting its viability as a low-cost method to support youth. Big Brothers Big Sisters Canada (BBBSC) runs free mentorship programs for youth and, during the pandemic, matches used technology to continue communication. The present study investigates how BBBSC mentees experienced this transition to virtual or hybrid services using a qualitative approach. Methods. Mentees (ages 12-18) were self-selected by a parent who completed a brief demographic survey. Select mentees (n = 7) participated in a semi-structured interview via Zoom about how they connected with their mentors, their experience communicating via technology, and their level of responsibility in their match. Thematic analysis will be used to analyze the interview transcripts. Analysis and results. Data analyses are still in progress; however, it is expected that technology may have posed barriers to connection in mentorship relationships. Mentees may also report both benefits and drawbacks of communicating via technology. Conclusions. Findings will address the feasibility of virtual mentorship programs and describe the benefits and barriers to the connection which will guide organizations and policymakers to maximize benefits for youth."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".