Development of brief assessment packages of psychosocial constructs related to doping
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".