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Record W4224992832 · doi:10.3389/fspor.2022.884239

Black Women Coaches in Community: Promising Practices for Mentorship in Canada

2022· article· en· W4224992832 on OpenAlexaffabout
Janelle Joseph, Alex I. McKenzie

Bibliographic record

VenueFrontiers in Sports and Active Living · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMentorshipCoachingMedical educationThematic analysisGeneral partnershipLonelinessPsychologyMedicinePublic relationsPolitical scienceQualitative researchSociologySocial psychologySocial science

Abstract

fetched live from OpenAlex

Mentorship programs have been shown to help under-represented women navigate their environments, but little research has been done on mentorship programs in sport coaching in Canada. The first of its kind in Canada, the Black Female Coach Mentorship Program (BFCMP) created by the Black Canadian Coaches Association in partnership with the Coaching Association of Canada caters to an historically excluded population: Black, Biracial, and Indigenous women coaches. The research aimed to understand the experiences of program participants to better inform policy, decision-making, and sustainability of the BFCMP. Through mentorship session observations, one-on-one semi-structured interviews with 15 of the 27 inaugural BFCMP mentors and mentees, and thematic analysis, we determined the ability to form a trusted community was a promising practice for coach mentorship programs. Our findings suggest that participants, the majority of whom were the only Black woman coach in their program/institution, benefit from mentorship because of the opportunities to help each other develop as leaders, build relationships to resist loneliness, and nurture resilience through community.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.029
GPT teacher head0.274
Teacher spread0.245 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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