Study protocol for Goodform - a classroom-based intervention to enhance body image and prevent doping and supplement use in adolescent boys
Bibliographic record
Abstract
BACKGROUND: Very few programs aimed at improving body image among adolescent boys have been effective, and there is still no clear evidence as to what will work for universal prevention of eating disorders and body dissatisfaction with this group. We combined two previously efficacious programs and used a design thinking framework to optimise program content alongside potential end-users including adolescent boys, teachers, parents, and experts. Goodform is a four-session universal program that aims to reduce body dissatisfaction and prevent the use of muscle-building supplements among 14-to-16 year old adolescent boys. METHODS/DESIGN: Goodform will be trialled using a cluster randomised controlled trial (RCT) conducted in Australian schools, with Year 9 boys as participants. The intervention is teacher-delivered. Data will be collected at three time points: baseline, post-intervention, and follow-up (2 months). Three primary outcome constructs will be examined, including body dissatisfaction (Male Body Attitudes Scale-Revised) and attitudes towards appearance and performance enhancing substances (APES; Outcome Expectations for Steroid and Supplement Use, Intentions to use APES) and actual use of APES at each time point. Three secondary outcome constructs will be examined, which are social norms for APES (adapted Peer Norms Scale), negative body talk (Male Body Talk Scale), and internalisation of and pressure to attain appearance ideals (Sociocultural Attitudes Towards Appearance Questionnaire-4 Revised). Internalisation of appearance ideals will also be examined as a mediator of change in primary outcomes. Teachers will provide data on adherence to lessons, student engagement/enjoyment, and understanding of the content. DISCUSSION: The GoodForm RCT will trial a novel, generalizable, and extensively developed program intended to improve boys' body image and reduce actual and intended APES use. We anticipate that it will provide a novel contribution to the field of boys' body dissatisfaction prevention. TRIAL REGISTRATION: This trial was retrospectively registered with the Australian and New Zealand Clinical Trials Registry on May 14th 2019, registration number ACTRN12619000725167.
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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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.166 | 0.028 |
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".