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Record W2057859517 · doi:10.1055/s-2008-1038403

Athletes' Knowledge and Views on OTC Medication

2008· article· en· W2057859517 on OpenAlexaboutno aff
David R. Mottram, N Chester, Greg Atkinson, Dulcie Goode

Bibliographic record

VenueInternational Journal of Sports Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesElite athletesMedicineAgency (philosophy)StimulantOver-the-counterFamily medicinePhysical therapyPharmacologyMedical prescription

Abstract

fetched live from OpenAlex

A questionnaire was administered to elite athletes from Australia, Canada, the UK, and the USA representing 10 Olympic sports in order to explore knowledge and understanding of over-the-counter (OTC) medication since the removal of many of these substances from the World Anti-Doping Agency (WADA) Prohibited List, in 2004. Athletes demonstrated limited knowledge and understanding. Around half (50.5 %) knew the penalty incurred following a doping violation involving a banned OTC stimulant. The terms Monitoring Program and Specified Substance List were understood by 43.3 % and 67.5 % of respondents, respectively. Overall, the status of substances in relation to the Prohibited List was correctly identified in just 35.1 % of cases. As a whole, athletes were of the opinion that OTC stimulants posed a risk to health, were performance enhancing and that their use was against the spirit of sport. They were undecided as to whether these drugs should be returned to the Prohibited List. Elite athletes require targeted education programmes that will enable them to make informed decisions on the potential of OTC medications for therapeutic or performance enhancing purposes.

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)0.000
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 score0.664
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.043
GPT teacher head0.338
Teacher spread0.295 · 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

Citations34
Published2008
Admission routes1
Has abstractyes

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