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Record W4411549379 · doi:10.1123/iscj.2024-0001

Learning a Trauma-Sensitive Sport Model: Training Workshop Experiences

2025· article· en· W4411549379 on OpenAlexaff
Majidullah Shaikh, Diane M. Culver, Tanya Forneris

Bibliographic record

VenueInternational Sport Coaching Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of OttawaOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsTraining (meteorology)PsychologyComputer scienceMedical educationApplied psychologyMedicineGeography

Abstract

fetched live from OpenAlex

The purpose of this study was to explore leaders’ development of trauma-sensitive approaches in their sport coaching practices through exploring the learning experiences of leaders of a national community youth-serving organisation who participated in an initial training workshop. The value-creation framework was used to explore learning experiences based on the interactions and values leaders discussed. Two training workshops were held with two different cohorts of leaders. The researcher observed these workshops, and the leaders were interviewed about their preferences for training and development, and their experiences of learning in the workshops. The interview data were analysed collectively using a deductive–inductive thematic analysis. Data were also collected on leaders’ satisfaction with training, knowledge perceptions, content knowledge, and attitudes toward trauma-sensitive practice. These data were analysed using descriptive statistics and Wilcoxon signed-ranks tests. Collectively, the results showed that the leaders (a) improved their relevant knowledge from pre- to postparticipation; (b) valued having a variety of learning opportunities; (c) wanted to learn how to support their youth’s needs better within and beyond sport; and (d) intended to promote more responsive, trauma-sensitive programme practices. Implications were discussed for the improvement of training opportunities to meet learners’ needs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.334
Teacher spread0.310 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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