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Record W3040200511 · doi:10.3389/fpsyg.2020.01564

The Susceptibles, Chancers, Pragmatists, and Fair Players: An Examination of the Sport Drug Control Model for Adolescent Athletes, Cluster Effects, and Norm Values Among Adolescent Athletes

2020· article· en· W3040200511 on OpenAlexfundno aff
Adam R. Nicholls, Andrew R. Levy, Rudi Meir, Colin Sanctuary, Leigh Jones, Timothy Baghurst, Mark A. Thompson, John L. Perry

Bibliographic record

VenueFrontiers in Psychology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsAthletesPsychologyNorm (philosophy)Cluster (spacecraft)Control (management)Social psychologyDevelopmental psychologyPhysical therapyMedicineEpistemology

Abstract

fetched live from OpenAlex

Although there are few high-profile cases of adolescent athletes being caught doping, up to a third of young athletes may dope. In order to generate a more accurate understanding of why adolescent athletes dope, it is important to validate models that help to explain this behavior. The aims of this study were 3-fold: firstly, to test the Sport Drug Control Model for Adolescent Athletes (SDCM-AA); secondly, to generate athlete profiles that would help quantify the proportion of athletes who are at risk of doping; and thirdly, to create norm values for the Adolescent Sport Doping Inventory (ASDI), which would allow national doping organizations, sporting organizations, and clubs to benchmark the scores of their athletes for key psycho-social variables linked to doping. A total of 2208 adolescent athletes from the United Kingdom, Australia, Hong Kong, and the United States completed the ASDI. The data presented an appropriate fit to the SDCM-AA model, in which 54% of the variance in susceptibility to doping was explained in the model, and 44.8% of attitudes toward doping was accounted for. Four distinct clusters of athletes emerged: the Susceptibles (i.e., identified with the benefits of doping, were willing to cheat, and viewed little threat), the Chancers (i.e., identified with the benefits of doping, scored high on willingness to cheat, and were highly influenced by their reference group, but had an average score for threat, self-esteem, and legitimacy), the Pragmatists (i.e., did not engage with any aspects of doping, but were more susceptible than the fair players), and Fair Players (i.e., high levels of sportspersonship, unwilling to cheat, and viewed doping as a threat). The revised SDCM-AA appears a valid model that helps explain the factors associated with doping attitudes and doping susceptibility. Adolescent athletes can be classified into one of four clusters, in relation to doping. Their cluster group could influence the content of the anti-doping education they receive.

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)0.015
metaresearch head score (Gemma)0.039
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 score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0020.004
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.002
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 head0.013
GPT teacher head0.283
Teacher spread0.270 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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