The Formation and Benefits of Natural Mentoring for African American Sexual and Gender Minority Adolescents: A Qualitative Study
Bibliographic record
Abstract
This study explored how mentoring begins and the benefits provided for African American sexual and/or gender minority (SGM) youth. Participants were mentors and mentees living in three Mid-Atlantic cities. Mentees (ages 15–21, n = 14) identified as African American; cisgender male, transgender female, or non-binary assigned male; and had sexual interest in men. Mentor participants (ages 18+, n = 13) mentored such youth. Qualitative in-depth interviews were conducted with mentoring relationship partners (both partners did not necessarily participate). All interviews were audio-recorded, transcribed, and imported into Atlas.ti. Using a basic interpretive qualitative analysis, a codebook was developed through inductive and deductive techniques. Analysis focused on mentees’ and mentors’ descriptions and interpretations about how they formed a mentoring relationship and any observed benefits that arose. Themes showed mentoring relationships were formed through introductions via social circles or social media. Mentoring was described as providing a trusted confidant and support with identity formation, relationships, transitioning to adulthood, and health. Results indicate a potential for natural mentoring relationships to provide trusted adult support to SGM adolescents in ways that are experienced as authentic and beneficial to the mental health of African American SGM male youth.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".