Perceptions and Aspirations Toward Peer Mentoring in Social Media–Based Electronic Cigarette Cessation Interventions for Adolescents and Young Adults: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Social media offer a promising channel to deliver e-cigarette cessation interventions to adolescents and young adults (AYAs); however, interventions delivered on social media face challenges of low participant retention and decreased engagement over time. Peer mentoring has the potential to ameliorate these challenges. OBJECTIVE: The aim of this study was to understand, from both the mentee and potential mentor perspective, the needs, expectations, and concerns of AYAs regarding peer mentoring to inform the development of social media-based peer mentoring interventions for e-cigarette cessation among AYAs. METHODS: Seven focus groups, including four mentee groups and three potential mentor groups, were conducted with 26 AYAs who had prior experience with e-cigarette use and attempts to quit in the context of a social media-based e-cigarette cessation intervention. Discussion focused on preferred characteristics of peer mentors, expectations about peer mentoring, mentoring mode, mentor training, incentives for peer mentors, preferred social media platforms for intervention delivery, supervision, and concerns. Focus group transcripts were coded and analyzed using a thematic analysis approach. RESULTS: Overall, participants were receptive to peer mentoring in social media-based cessation interventions and believed they could be helpful in assisting e-cigarette cessation. Participants identified the most important characteristics of peer mentors to be of similar age and to be abstinent from e-cigarette use. Participants expected peer mentors would share personal experiences, provide emotional support, and send check-ins and reminders. Peer mentors supporting a group of mentees in combination with one-on-one mentoring as needed was the preferred mentoring mode. A group of 10 mentees with a mentor:mentee ratio of 1:3-5 was deemed acceptable for most participants. Participants expressed that mentor training should include emotional intelligence, communication skills, and the scientific evidence about e-cigarettes. Although monetary incentives were not the main motivating factor for being a peer mentor, they were viewed as a good way to compensate mentors' time. Instagram was considered an appropriate social media platform to deliver a peer-mentored intervention due to its functionality. Participants did not express many privacy concerns about social media-based peer mentoring, but mentioned that boundaries and community agreements should be set to keep relationships professional. CONCLUSIONS: This study reflects the needs and preferences of young people for a peer mentoring intervention to complement a social media program to support e-cigarette cessation. The next step will be to establish the feasibility, acceptability, and preliminary efficacy of such a peer mentoring program.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".