Cultivating Connections: Understanding the Impact of Mentors on Young Adult’s Flourishing
Bibliographic record
Abstract
Young adulthood is a unique time of exceptional cognitive, social and emotional development. \nThroughout this time, various opportunities and challenges exist, including increased transitions, \nindependence, responsibilities, identity development and new relationships; all which can positively or \nnegatively impact an individual’s well-being. Understanding how to mitigate the adverse impacts and \nenhance the positive ones is crucial. Mentorship may be an effective method of support during this time. \nWhile research on adolescent mentorship consistently demonstrates that strong mentee and mentor \nrelationships facilitate mentee well-being, less is known about the benefits during young adulthood. \nEvidence suggests that social connectedness and strong relationships via mentoring would benefit an \nindividual entering adulthood, therefore, there is a need for more research on this topic, specifically around \nthe mentor’s impact on a young adult’s well-being. In this thesis, data were utilised from a mixed-methods \nanonymous questionnaire delivered to young adults in Canada (age: M = 24.2, SD = 2.57, 70.9% female). \nThe quantitative data was used to understand their well-being experiences (i.e., flourishing) and if the \npresence of a mentor, participant’s gender or adulthood status influences young adults’ flourishing levels. \nIn addition, the thesis explored the qualities and characteristics of young adults’ mentors via open-ended \nqualitative responses. The results found no relationship between the presence of a mentor, mentoring \nrelationship quality, or one’s gender associated with the level of participant’s well-being. However, the \nresults indicated a significant difference between the well-being levels of individuals who identify as adults \nand those who do not. There was a significant positive relationship between adult status and well-being. \nThe qualitative analysis provided insight into the mentors’ qualities and actions that were most important \nto participants, and a clear theme of intentional connection emerged. The research identified differences \nbetween adolescent and adult mentoring relationships, including differences in social power, mentees' \nneeds, and the overall purpose of these relationships. Overall, the results provide new insight into \nmentorship through the lens of young adults. The findings point to important applications for communities \nand organisations that work with or support young adults, in addition to ideas for future research in this \nfield of study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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 teacher head, 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".