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A qualitative analysis of university athlete’s perceptions and negotiations of health and athletic participation

2014· dissertation· en· W24861172 on OpenAlexaboutno aff
Nichole Adams

Bibliographic record

VenueJAMA Internal Medicine · 2014
Typedissertation
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute on Aging
KeywordsAthletesBasketballFocus groupContext (archaeology)PsychologyQualitative researchNegotiationMedical educationConstruct (python library)PerceptionApplied psychologySociologyMedicinePhysical therapySocial science

Abstract

fetched live from OpenAlex

This qualitative study explores student-athletes’ relationships with food and exercise, often referred to as eating and training thus unpacking how student-athletes come to understand health within the context of university sport. The unique nature of the study, with its focus on intercollegiate sport in Canada, contributes to the lack of knowledge about Canadian sport athletes’ experiences of sport, and it provides a qualitative lens to consider how student-athletes construct notions about the body, health and performance. Seventeen (eight female and nine male) student-athletes, from Memorial University 2010-2011 varsity roster participated in this study. Participants were representative of varsity sports offered at the university, including individual sports: cross-country running, swimming and wrestling; and team sports: basketball, soccer and volleyball. Data collection involved four focus group discussions and follow-up semi-structured interviews with four individual student-athletes. Using discourse analysis, informed by Foucault’s concepts of the panopticon and technologies of the self, data analysis exposed how cultures of sport not only shape student-athletes understandings of eating and training but also how sport normalizes and regulates specific (un)healthy ideas and practices.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.240
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.049
GPT teacher head0.413
Teacher spread0.364 · 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
Published2014
Admission routes1
Has abstractyes

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