Does a person's body size and the application type influence healthcare students' perceptions of technologies to promote physical activity? Findings from a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Recent evidence suggests that weight bias may be pervasive, even among health professionals specialized in obesity, including healthcare students. Technology-based physical activity interventions are promising for people with obesity, specifically when they are theory-driven (e.g., autonomy-supportive as described by self-determination theory). However, perceptions of these technologies have been understudied among healthcare students and professionals. OBJECTIVE: The purpose of this study was to examine the influence of a person's body size based on body mass index and technology type on healthcare students' perceptions. DESIGN: This is a cross-sectional, experimental study. PARTICIPANTS AND METHODS: ) and a technology-based physical activity type based on self-determination theory (autonomy-supportive app vs. controlling app). They then completed measures of their perceptions of the person's app acceptability and self-efficacy and of their intention to recommend the app. Multivariate and univariate analyses of covariance were performed. RESULTS: ) perceived a lower level of person's app acceptability (i.e., higher social influence and less enjoyment in using the app), as well as a lower level of self-efficacy to use the technology. Students exposed to the controlling app were more likely to recommend it compared to those exposed to the autonomy-supportive app. CONCLUSIONS: These results suggest that healthcare students' attitudes may be negatively influenced by explicit weight bias. Also, in contrast to self-determination theory precepts, a controlling app may be more frequently recommended. Further study of healthcare students' implicit attitudes toward technology is needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".