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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".