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Record W2924881771 · doi:10.1017/jrr.2019.1

When Insecure Attachment Dispositions Affect Mentoring Relationship Quality: An Exploration of Interactive Mentoring Contexts

2019· article· en· W2924881771 on OpenAlexaff
Simon Larose, George M. Tarabulsy, Geneviève Boisclair Châteauvert, Michael J. Karcher

Bibliographic record

VenueJournal of Relationships Research · 2019
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAmbivalencePsychologyAffect (linguistics)Context (archaeology)Quality (philosophy)Emotional supportSocial psychologyDevelopmental psychologyMedical educationSocial supportMedicineCommunication

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.284
GPT teacher head0.544
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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