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Record W4404107952 · doi:10.1080/09687637.2024.2423753

Development of brief assessment packages of psychosocial constructs related to doping

2024· article· en· W4404107952 on OpenAlexfundno aff
Nikos Ntoumanis, Vassilis Barkoukis, Anne Marte Pensgaard, Andréas Ivarsson, J.T. Rivold

Bibliographic record

VenueDrugs Education Prevention and Policy · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsPsychosocialPsychologyApplied psychologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Background We aimed to develop brief self-report tools that can be used by anti-doping organizations (ADOs) to evaluate education programs regarding their effects on psychosocial correlates of doping. Current assessment tools are too long for this purpose.Methods In phase 1, we reviewed the literature and selected psychosocial constructs perceived to be amenable to anti-doping education. In phase 2, a survey with these constructs was sent to anti-doping experts (i.e. researchers and representatives of ADOs), who rated their importance and rank ordered them. Following this, a smaller pool of constructs was chosen for phase 3, during which, questionnaires capturing these constructs were distributed to adult athletes and athlete support personnel (ASP) in Denmark, Norway, and Sweden.Results Using data from 307 adult athletes and 296 ASP, we selected the best 2–3 items for scales tapping each construct, via the OASIS package in R. Two questionnaires with 24 (athletes) and 28 (ASP) items, respectively, were formed. The questionnaires assess 11 and 13 different constructs, respectively, and capture diverse aspects such as morality, motivation, perceived benefits, and perceived deterrents relevant to doping.Conclusion The questionnaires provide brief assessments for diverse psychosocial constructs that could be used to evaluate ADO education programs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.421
Teacher spread0.404 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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