Exploring the relationship between physical activity intensity and body appreciation in \nadolescent girls
Bibliographic record
Abstract
As a result of the COVID-19 pandemic, physical activity (PA) levels have sharply \ndeclined among youth. This comes at a time when PA engagement is already low, but it has been \nreported that less than one in five children and youth currently meet the Canadian movement \nguidelines. This is concerning given the mental and physical health benefits associated with PA \nparticipation, which include improved cardiorespiratory fitness, muscular strength/endurance, \ncognitive functioning, and psychosocial health. It is important to continue to examine the factors \nthat contribute to engagement in and disengagement from PA. Among girls, one prominent \nfactor contributing to disengagement from PA is body image, which generally happens during \nthe transition to adolescence. To date, most research focuses on interventions aimed at improving \nnegative body image in PA and sport. However, these interventions neglect the importance of \ncultivating a positive body image among individuals. More research is needed to understand the \nrelationship of PA intensity in helping adolescent girls cultivate a positive body image. This \nresearch examines the relationship between PA participation and body appreciation and the role \nthat exercise intensity plays in helping adolescent girls cultivate body appreciation. Participants \nincluded girls aged 13 to 17 years attending school in Newfoundland and Labrador. In this \nmixed-methods study, physical activity was measured using accelerometry and body \nappreciation was assessed using the Body Appreciation Scale (BAS-2). Semi-structured \ninterviews were also conducted to examine the influence of physical activity participation on \nbody appreciation in girls. Descriptive statistics were computed for both the BAS-2 as well as \naverage minutes of physical activity per day. Correlational analyses were used to examine relationships between physical activity and body appreciation. A thematic analysis was \nconducted to analyze the interview data. Although no significant relationship was established for \nphysical activity participation and body appreciation in adolescent girls, the qualitative results \nfrom the interviews suggest that participating in moderate to vigorous physical activity intensity \nin addition to light physical activity promotes a more positive perception of one’s body image.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".