The development and validation of the Adolescent Sport Drug Inventory (ASDI) among athletes from four continents.
Bibliographic record
Abstract
A significant barrier to understanding the psychosocial antecedents of doping use among adolescent athletes is the lack of valid measures. In order to address this issue, the first aim of this paper was to develop and validate the Adolescent Sport Drug Inventory (ASDI) among adolescent athletes from Asia, Europe, North America, and Oceania. The second aim was to assess the construct validity of the ASDI. As such, this paper is divided into two parts. Part 1 relates to the development of the ASDI and contains two studies: item development (Study 1) and factorial validity (Study 2). Part 2 contains information on how the psychosocial variables measured in the ASDI are associated with situational temptation, and honesty (Study 3), maturation (Study 4), stress and coping (Study 5), and coaching (Study 6). In devising the ASDI, 19 different models were examined, which culminated in a 9-factor, 43-item ASDI. Coping, mastery-approach goals, and cognitive-social maturity were associated with doping attitudes. Caring motivational climates, strong coach-athlete relationships, and positive coach behaviors were associated with athletes being less susceptible toward doping, which provides construct validity for the ASDI. The ASDI is a valid tool to assess the psychosocial factors associated with doping among adolescent athletes. This questionnaire can be used to identify athletes who are the most at risk of doping, assess how the psychosocial factors associated with doping change over time, and to monitor the impact of antidoping interventions for adolescent athletes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".