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Record W2512005546 · doi:10.1002/jcop.21782

MENTORING RELATIONSHIPS, POSITIVE DEVELOPMENT, YOUTH EMOTIONAL AND BEHAVIORAL PROBLEMS: INVESTIGATION OF A MEDIATIONAL MODEL

2016· article· en· W2512005546 on OpenAlexafffundabout
Gizem Erdem, David L. DuBois, Simon Larose, David De Wit, Ellen L. Lipman

Bibliographic record

VenueJournal of Community Psychology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsMcMaster UniversityCentre for Addiction and Mental HealthUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsPsychologyPositive Youth DevelopmentEmotional competenceAssociation (psychology)Structural equation modelingDevelopmental psychologySocial emotional learningCompassionCompetence (human resources)Clinical psychologyEmotional intelligenceSocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Mentoring programs show promise for preventing emotional and behavioral problems among at‐risk youth, but little is known about processes that may be most critical to achieving this end. This study explored indicators of positive youth development (PYD; competence, confidence, connection, care and compassion, character) as mediators of associations of mentoring support with youth emotional and behavioral problems. The sample included 501 youth participating in a study of the Big Brothers Big Sisters program in Canada (mean = 11.16 years old; 52% girls, and 44% White). Measures were youth self‐report, with the exception of the use of both youth and parent report measures of emotional and behavioral problems. Results of structural equation modeling analyses were consistent with PYD mediating the association between mentoring support and emotional and behavioral problems, regardless of informant. The association between mentor support and PYD, however, was limited to youth in active mentoring relationships.

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)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.218
GPT teacher head0.393
Teacher spread0.175 · 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 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

Citations52
Published2016
Admission routes3
Has abstractyes

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