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Record W3138538457 · doi:10.1080/07448481.2021.1897014

Tobacco use among varsity athletes – why do they do it and how do we make it stop: a brief report

2021· article· en· W3138538457 on OpenAlexaff
Sarah Deck, Taniya S. Nagpal, Anisa Morava, Jade Farhat, Federico Cisneros Sánchez, Harry Prapavessis

Bibliographic record

VenueJournal of American College Health · 2021
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of OttawaUniversity of AlbertaWestern University
Fundersnot available
KeywordsAthletesExploratory researchTobacco usePsychologyPerceptionEnvironmental healthMedicinePhysical therapySociology

Abstract

fetched live from OpenAlex

The current study explores the motivations underlying tobacco use among varsity athletes. A cross-sectional exploratory mixed method approach was used. Both tobacco users (TU) and non-tobacco users (NTU) completed an online survey of closed and open-ended questions. These questions focused on tobacco use, motivations for tobacco use, teammate and coaches' perceptions of athlete tobacco use, and self-perceived effects of tobacco use on health and athletic performance. Thirty-eight completed surveys were included of which 12 were TU and 26 were NTU. The majority of TU indicated that they used products during the off-season. Motivations for using tobacco products included social influences, stress-relief, and increasing energy. TU mostly indicated that there are negative effects on their health but not on their athletic performance, whereas NTU reported potential detrimental effects on their teammate's performance. Overall, varsity athletes who use tobacco products are aware of the health effects and negative opinions of their teammates.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.951

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.376
Teacher spread0.316 · 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 designNot applicable
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

Citations6
Published2021
Admission routes1
Has abstractyes

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