Determining Associations Among Health Orientation, Fitness Orientation, and Attitudes Toward Fatness in Physiotherapists and Physiotherapy Students Using Structural Equation Modeling
Bibliographic record
Abstract
Purpose: Research suggests physiotherapists hold negative attitudes and beliefs toward fatness and fat people. Physiotherapists are also health-conscious, and invested in healthy lifestyle behaviours including physical activity. Our purpose was to describe relationships between health orientation, fitness orientation, and fat attitudes. Methods: Physiotherapists ( n = 187) and physiotherapy students ( n = 34) completed an online survey (Health Orientation Scale, Multidimensional Body-Self Relations Questionnaire, Fat Attitudes Assessment Toolkit). Structural equation modeling estimated associations between fat attitudes (dependent variable) and health and fitness orientation (independent variables). Results: Participants scored high in orientation toward fitness and health. We found strong positive associations between fitness orientation and health orientation ( p < 0.001). Health orientation was not significantly associated with fat attitudes ( p = 0.075), whereas increased age was associated with more positive fat attitudes ( p < 0.01). Although most participants acknowledged that factors outside an individual’s control contribute to body weight, many also agreed with normative negative perspectives. Conclusions: Physiotherapists are highly oriented toward fitness and health. This may underlie beliefs in the controllability of body weight and contribute to negative attitudes toward fatness and fat people. Further research, with greater sample sizes is necessary to further investigate associations between health orientation and fat attitudes.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".