The aetiology and trajectory of anabolic-androgenic steroid use initiation: a systematic review and synthesis of qualitative research
Bibliographic record
Abstract
BACKGROUND: To our knowledge, there has never been a systematic review and synthesis of the qualitative literature on the trajectory and aetiology of nonmedical anabolic-androgenic steroid (AAS) use. METHODS: We systematically reviewed and synthesized qualitative literature gathered from searches in PsycINFO, PubMed, ISI Web of Science, Google Scholar, and reference lists of relevant literature to investigate AAS users' ages of first use and source(s), history prior to use, and motives/drives for initiating use. We adhered to the recommendations of the UK Economic and Social Research Council's qualitative research synthesis manual and the PRISMA guidelines. RESULTS: A total of 44 studies published between 1980 and 2014 were included in the synthesis. Studies originated from 11 countries: the United States (n = 18), England (n = 8), Australia (n = 4), Sweden (n = 4), both England and Wales (n = 2), and Scotland (n = 2). One study each originated from Brazil, Bulgaria, Canada, France, Great Britain, and Norway. The majority of AAS users initiated use before age 30. Sports participation (particularly power sports), negative body image, and psychological disorders such as depression preceded initiation of AAS use for most users. Sources of first AAS were mainly users' immediate social networks and the illicit market. Enhanced sports performance, appearance, and muscle/strength were the paramount motives for AAS use initiation. CONCLUSIONS: Our findings elucidate the significance of psychosocial factors in AAS use initiation. The proliferation of AAS on the illicit market and social networks demands better ways of dealing with the global public health problem of AAS use.
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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 | 0.111 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".