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Record W2790136216 · doi:10.1016/j.peh.2018.01.001

Doping prevalence among Danish elite athletes

2018· article· en· W2790136216 on OpenAlexfundno aff
Anne‐Marie Elbe, Werner Pitsch

Bibliographic record

VenuePerformance Enhancement & Health · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsnot available
FundersWorld Anti-Doping AgencyAnti Doping Danmark
KeywordsDanishConfidence intervalAthletesElite athletesDemographyMedicineEliteGermanPhysical therapyInternal medicineGeographySociology

Abstract

fetched live from OpenAlex

The objective of this study was to investigate seasonal and all time doping among Danish elite athletes (N = 771, male = 56.5%) and to investigate gender differences. An online survey was conducted (response rate = 57%) which included biographical information as well as randomized response technique questions about seasonal and all-time doping. Concerning last season prevalence, the maximum doping rate was estimated at 30.6% (95% confidence interval 22.6–35.7) and the rate of honest non-dopers was estimated at 69.4%. For the lifetime prevalence of doping, a rate of at least 3.1% dopers (95% confidence interval 0–8.9) and a maximum of 26% (95% confidence interval 13.4–40.8), with a rate of approximately 74% who can reliably be estimated to have never doped throughout their career was identified. No significant gender differences were found. In conclusion, the doping prevalence among Danish elite athletes is similar to that of Dutch and German elite athletes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.338
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), 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

Citations54
Published2018
Admission routes1
Has abstractyes

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