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Record W4321609481 · doi:10.1123/cssep.2022-0013

Constellation Mentoring for University Soccer Players: A Case Study

2023· article· en· W4321609481 on OpenAlexaffabout
Brennan Petersen, Cole E. Giffin, Thierry R. F. Middleton, Yufeng Li

Bibliographic record

VenueCase Studies in Sport and Exercise Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsLaurentian University
Fundersnot available
KeywordsFeelingPeer mentoringThematic analysisPsychologyAthletesConstellationIntervention (counseling)Cohesion (chemistry)Applied psychologyMedical educationQualitative researchSocial psychologyPedagogyMedicine

Abstract

fetched live from OpenAlex

Peer mentoring is a supportive relationship between a more experienced mentor and a less experienced protégé that has garnered attention in recent sport research. Moving beyond traditional mentoring dyads, constellation peer mentoring engages several mentors to provide support to a protégé, ensuring they have access to multiple perspectives and areas of expertise. We implemented a constellation peer-mentoring program with Canadian university student-athletes throughout their competitive seasons. Subsequently, we undertook an instrumental case study to explore participants’ feedback and the perceived benefits of the program. Using reflexive thematic analysis, we interpreted participants’ responses as indicative of traditional mentoring benefits, including reduced transitional stress, feelings of well-being, and feelings of satisfaction. In addition, we determined unique aspects of constellation peer mentoring, such as increased team cohesion, improved help-seeking, an environment that fostered relational mentoring experiences, and the need for leader training. Constellation peer mentoring presents a promising intervention for supporting student-athletes during career transitions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.110
GPT teacher head0.428
Teacher spread0.317 · 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.

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

Citations4
Published2023
Admission routes2
Has abstractyes

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