Developing Questionnaire Items for Assessing Gender- and Body -Inclusive Body Ideals
Bibliographic record
Abstract
Background: Body image research has traditionally been in the heteronormative binary-gender paradigm with an exclusive focus on thin- and muscular-body ideals. Consequently, the field is criticized for its lack of inclusivity and diversity. To incorporate gender- and body-inclusive research, a measurement that is applicable to all gender and body type is needed. Purpose: To develop measurement items that assess gender- and body-inclusive body ideals. Methods: This study consisted of three stages: 1) item generation based on literature and existing items, 2) item refinement via focus group discussion (~30 min) with four graduate students at the researchers’ institution and a subsequent thematic analysis, and 3) item face validation by two experts at the researchers’ institution. Results/findings: Two items were generated and refined based on stage 2) and 3). The main item reads, “Thinking about your ideal body, which of the following would best describe it?” Response options include: “Thin (low body fat/slender)”, “Slim-thick (slim-curvy/ hourglass figure/slim/dad bod)”, “Fat (larger all around/plus-size model figure/voluptuous-curvy/bulky/paunchy dad bod)”, ‘”Lean-muscular (toned figure)”, “Bulky-muscular (prominent muscles/ bodybuilder figure)”, “No preference”, “Do not know”, “Prefer not to answer”, and “None of the above”. Conclusions: The items developed via this work will help identify various body ideals without classifying respondents into traditional binary gender constructs or making assumptions about normalized body ideal standards. Not only can the items be used in body image research, but they may also serve as a valuable tool for predicting exercise/sports participation in relation to body image regardless of gender and body type.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".