Conflict between Women's Physically Active and Passive Leisure Pursuits: The Role of Self‐Determination and Influences on Well‐Being
Bibliographic record
Abstract
BACKGROUND: Evidence in support of both physically active and passive leisure as significant contributors to well-being has surfaced around the world. However, for physically active, working mothers, fitting leisure into an already busy schedule can be challenging. The purpose of this study was to examine the influence of time resources and self-determination for active and passive leisure on conflict between these two leisure domains and the influence of this conflict on well-being. METHODS: A total of 66 working mothers completed validated questionnaires measuring satisfaction with time and motivation at baseline followed by two weeks of computerized diary capture to evaluate leisure engagement with final measures of goal conflict and well-being at the end of the two weeks. RESULTS: Results indicated that dissatisfaction with time resources is associated with increased goal conflict as are non-self-determined motivation for physically active leisure and self-determined motivation for passive leisure. Controlling for engagement in physically active and passive leisure, well-being is negatively influenced by goal conflict. CONCLUSIONS: Time resources, goal conflict, and motivation are important factors to consider in efforts to increase well-being among physically active working mothers. Further research is required to understand the influence of opposing motivational orientations on goal conflict.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".