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Record W4408200138 · doi:10.1080/13573322.2025.2476601

Peeling back the layers: examining the trauma-informed programming experiences of staff in a sport for development and peace organization

2025· article· en· W4408200138 on OpenAlexafffundabout
Dallas B. Ansell, Paul Nya, Jay Scherer, Nancy Spencer-Cavaliere, Nicholas L. Holt, A. C. Monahan

Bibliographic record

VenueSport Education and Society · 2025
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyPhysical educationProfessional developmentMedical educationPedagogyApplied psychologyPublic relationsSocial psychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

An increasing number of Sport for Development and Peace (SDP) organizations are aiming to offer trauma-informed programming (TIP). However, there is limited understanding of the specific efforts made by SDP staff to put TIP into practice. This paper explores the implementation of TIP by staff at Free Play (FP), a non-profit SDP organization based in Edmonton, Alberta. Drawing from a two-year, Community-Based Research (CBR) project, we critically analyze the experiences of FP staff as they navigate the entanglements of the ethical imperative of delivering TIP, and a broad array of social services to youth and their families, alongside the organization’s growth agenda within the competitive non-profit industrial complex shaped by the market-driven logic of neoliberalism. These contradictions set decisive limits and pressures on staff who recognize the value and importance of TIP, but who struggle with the heavy emotional labour of implementing it, especially without ongoing training and support, and while working under precarious conditions. Our analysis, in turn, provides recommendations for managers and staff of SDP organizations on how to better support TIP implementation.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.213

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 integrity0.0000.000
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 head0.030
GPT teacher head0.351
Teacher spread0.320 · 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 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

Citations0
Published2025
Admission routes3
Has abstractyes

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