Seven points as an estimate of the smallest subjectively experienced decrease in body satisfaction on a one-item Visual Analogue Scale
Bibliographic record
Abstract
Visual Analogue Scales (VASs) are very commonly used to measure short-term effects on state body satisfaction, the in-the-moment subjective evaluation of one’s own body. However, VASs lack easily understood metrics for comparing and interpreting the size of different effects, with the result that researchers often conclude that any statistically significant change on these 101-point scales is practically important. In addition to test-retest reliability and construct validity, here we estimate the smallest subjectively experienced difference for a one-item body satisfaction VAS. Seven points of change on the VAS was a useful cut-off for distinguishing participants who subjectively experienced no change ( n = 603) versus those who experienced at least a little decrease in body satisfaction ( n = 301) between two timepoints. With reference specifically to media influences on body satisfaction, we show how the smallest subjectively experienced difference may be used as an easily interpreted effect size metric when comparing and interpreting the size of different effects, as well as determining who is, and who is not, subject to those effects. We highlight how having this metric available to researchers can aid in the exploration and communication of different short-term influences on state body satisfaction. • Visual Analogue Scales (VASs) are often used to measure state body satisfaction. • It is unknown what change on 101-point VASs is meaningful vs. too small to notice. • How many points of change do people experience as “a little less satisfaction”?. • A decrease of ≥ 7 points was likely to be experienced as at least a little less. • 7 points could be used as a yardstick for comparing influences on body satisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".