“If You Don’t Have Anything Nice to Say, Then Don’t Say Anything At All”: Positive Appearance-related Commentary and Physical Activity
Bibliographic record
Abstract
BACKGROUND: Despite the well-documented benefits of physical activity, North Americans remain insufficiently inactive. Consequently, determining what motivates individuals to engage in physical activity becomes increasingly important. The purpose of this study was to examine whether the frequency of negative appearance-related commentary and positive appearance-related commentary could predict physical activity behavior. METHODS: Participants were young adult women (N = 192) who completed a series of questionnaires to assess the frequency of appearance-related commentary they received and their physical activity behavior. RESULTS: A hierarchical regression analysis indicated the overall regression was significant, F (4,187) = 4.73, P < .001, R2 adj = .07, ΔR2= .07), with positive weight/shape appearance-related commentary (β = 470.27, P < .001) significantly predicting physical activity behavior, while controlling for body mass index. CONCLUSIONS: Providing positive reinforcement via positive weight/shape compliments may be beneficial to motivate physical activity participation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".