Experiences and Needs of Multicultural Youth and Their Mentors, and Implications for Digital Mentoring Platforms: Qualitative Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Mentoring programs (ie, programs that connect youths with adult volunteers) have been shown to improve outcomes across the behavioral, social, and academic domains of youth development. As in other European countries, mentoring programs have few traditions in Norway, where interventions for multicultural youths are usually profession driven and publicly funded. Faced with the risk of disparities in education and health, there is a need to better understand this group's experiences and requirements relative to mentoring. This would also serve as a basis for designing and implementing digital support. OBJECTIVE: The objective of this study was to gain insight into multicultural youth mentees' and adult mentors' experiences and needs in the context of an ongoing mentoring program, how digital support (electronic mentoring) might address these needs, and how such support could be designed and implemented. METHODS: The study used a qualitative approach, with data from 28 respondents (21 mentees and 7 mentors). In total, 4 workshops with mentees as well as semistructured interviews with mentees and mentors were conducted. The sessions were audio recorded, transcribed, and analyzed thematically. RESULTS: In total, 3 main themes were identified from the experiences and needs reported by the mentees and mentors. These included a need for connection, help in achieving goals, and the need for security and control. Subthemes encompassed a desire to socialize with others, balancing the nature of the relationship, paying it forward, building trust, sharing insights and information with peers, goal-oriented mentees and mentors wanting to assist with goal achievement, and the fundamental need for privacy and anonymity in the digital platform. CONCLUSIONS: The findings of this study are supported by the literature on traditional mentoring, while also offering suggestions for the design of digital solutions to supplement the in-person mentoring of multicultural youth. Suggestions include digital support for managing the mentee-mentor relationships, fostering social capital, and ways of ensuring security and control. Features of existing electronic health apps can be readily adapted to a mentoring program context, potentially boosting the reach and benefits of mentoring.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".