An examination of the relationship between passion and physical activity in breast cancer survivors
Bibliographic record
Abstract
While previous studies indicate a significant relationship between passion and well-being, there has been little research on this relationship in special population groups such as cancer. The purpose of the present study was to (1) examine the reliability of the passion scales for breast cancer survivors (BCS); (2) identify the activities that BCS reported as passionate; and (3) examine the association between harmonious and obsessive passion and physical activity participation and sedentary behavior in BCS. The dualistic model of passion (Vallerand et al., 2003) was used as a guiding conceptual framework. Women (n=122; mean age=55; 87% Caucasian) who were within 6 months post-treatment for breast cancer completed reliable and valid self-report scales for passion, physical activity, and sedentary behavior. Cronbach alpha coefficients were .84 for both the harmonious and obsessive subscales. Results indicate that 52 out of 122 (43%) participants reported sport and physical activity as their passionate activity, the most common being walking (58%), conditioning (17%), and swimming (10%). Pearson correlation analysis suggest that moderate-to-vigorous physical activity (MVPA) was positively correlated to both harmonious passion (r=.47) and obsessive passion (r=.23). Sedentary behavior (i.e., screen time) was positively correlated to harmonious passion (r=.20) but not obsessive passion (r=.03). Findings provide theoretical and practical applications for understanding the role of passion in the lives of BCS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".