When Insecure Attachment Dispositions Affect Mentoring Relationship Quality: An Exploration of Interactive Mentoring Contexts
Bibliographic record
Abstract
Abstract In this study, we explored the effects of mentor and mentee insecure attachment dispositions (ambivalence and avoidance) on mentoring relationship quality while considering the specific nature of the interactive mentoring context. Participants (N = 252 matches) were enrolled in the MIRES program, a one-year college-based mentoring program that matches late adolescent mentees (17-year-olds) with young adult mentors (23-year-olds), designed to facilitate the transition to college. Using data drawn from mentors’ logbooks (at nine time points), two interactive contexts were addressed: (1) situations involving mentee academic issues and mentor proactive academic support (academically oriented), and (2) situations involving mentee personal issues and mentor emotional support, and caring (emotionally oriented). Linear regression results showed that both mentors’ and mentees’ avoidance uniquely predicted lower reports of mentoring relationship quality, but especially in emotionally oriented matches and when their partners’ attachment ambivalence was high. In matches less focused on emotional support, mentors’ attachment avoidance interacted with mentees’ ambivalence to predict positive mentoring relationship quality. Theoretical, practical, and mentor training issues are discussed.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".