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Record W2945383035 · doi:10.1177/1556264619842782

The Use of Samples Originating From Doping Control Procedures for Research Purposes: A Qualitative Study

2019· article· en· W2945383035 on OpenAlexfundno aff
Thijs Devriendt, Amicia Phillips, Mahsa Shabani, Pascal Borry

Bibliographic record

VenueJournal of Empirical Research on Human Research Ethics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsAccreditationAgency (philosophy)Informed consentQuality assuranceControl (management)Quality (philosophy)Research ethicsMedical educationPsychologyBusinessMedicinePublic relationsPolitical scienceComputer scienceAlternative medicineSociologyMarketingPsychiatry

Abstract

fetched live from OpenAlex

Doping control samples may be used for research purposes by the World Anti-Doping Agency (WADA)-accredited laboratories after their compulsory storage period has expired. This study investigates opinions of stakeholders toward the governance of antidoping research on these samples and to evaluate the current framework. Semistructured interviews were conducted with stakeholders in antidoping research. The distinction between research and quality assurance in the International Standard for Laboratories (ISL) is neither well-understood nor interpreted uniformly by WADA-accredited labs. Most laboratories would not seek ethics approval for research on doping control samples. Interviewees considered that athletes should be better informed on what antidoping research can entail. A consistent and uniform approach toward the consent should be employed worldwide. Standards and safeguards should be implemented to reduce the risk of reidentification. Centralization of the Informed Consent Form in the ADAMS (Anti-Doping Administration & Management System) database would facilitate providing more information and allow the implementation of the right to withdraw.

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)0.261
metaresearch head score (Gemma)0.335
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2610.335
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0060.005
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0000.012
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 head0.892
GPT teacher head0.738
Teacher spread0.154 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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