Requisite Characteristics of a Mentor to Establish Positive Relationships in a Type One Diabetes Intervention from the Mentee’s Perspective
Bibliographic record
Abstract
Background: Diabetes has reached global epidemic proportions. Type 1 Diabetes (T1D) typically strikes in childhood and is now becoming more prevalent in young adults. Evidence suggests that proactive harnessing of the positive attributes of a peer-to-peer mentor-mentee relationship could help mediate and decrease prevalence, assist with better glycemic control, reverse nonadherence and provide psychosocial support and education to people with diabetes. Research Question: What are the requisite components of a mentor needed to establish an effective mentorship relationship in a peer-to-peer coaching intervention for young adults with type one diabetes from the mentee’s perspective? Methods: A qualitative research design was used with Sandelowski’s (2010) qualitative descriptive approach. The Right Who, Respect, Information gathering, Consistency, and Support (TRICS) model was used as a theoretical framework (Donlan et al., 2017). Sample: 20 young adults aged 18-30 with T1D were recruited through snowball sampling. One semi-structured interview was completed with each participant. Data Analysis: All interview data were audiotaped and transcribed verbatim and managed through NVIVO. Findings/Discussion: Three themes were revealed in the data; 1) T1D is a personal journey through self-realization and acceptance; 2) inconsistencies in social support systems and 3) a mentor- is a companion on the journey. One supplemental theme highlights the perceived impact of COVID-19 on participants T1D. Conclusion: Individuals with T1D perceived there is value in cultivating a mentored form of peer support. Developing and evaluating a mentor/mentee dyad as a supportive intervention for T1D adults transitioning to adult care is the next step for future research.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".