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Record W1943272854 · doi:10.1002/jcop.21700

THE ROLE OF RELATIONAL, RECREATIONAL, AND TUTORING ACTIVITIES IN THE PERCEPTIONS OF RECEIVED SUPPORT AND QUALITY OF MENTORING RELATIONSHIP DURING A COMMUNITY-BASED MENTORING RELATIONSHIP

2015· article· en· W1943272854 on OpenAlexafffundabout
Simon Larose, Julien Savoie, David J. DeWit, Ellen L. Lipman, David L. DuBois

Bibliographic record

VenueJournal of Community Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsMcMaster UniversityCentre for Addiction and Mental HealthUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsRecreationPerceptionPsychologyAssociation (psychology)Quality (philosophy)Positive relationshipApplied psychologyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Previous research has shown that activities between volunteer mentors and youth mentees are associated with relationship quality. Using data from a longitudinal investigation of Big Brothers Big Sisters (BBBS) community mentoring relationships across Canada, the current study investigated whether different types of activities (relational and skill, recreational, and tutoring) moderate the association between mentees’ perceptions of received support and subsequent relationship quality. The results showed that, irrespective of activity type, activity frequency was positively associated with perceptions of received support and relationship quality. More importantly, higher frequency of recreational activities strengthened the positive association between perceptions of received support and relationship quality, whereas higher frequency of tutoring activities weakened this association. The implications for the provision of relational and skill, recreational, and tutoring activities are discussed in relation to BBBS programs.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.230
GPT teacher head0.427
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations21
Published2015
Admission routes3
Has abstractyes

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