Put my mask on first: Mothers' reactions to prioritizing health behaviours as a function of self-compassion and fear of self-compassion
Bibliographic record
Abstract
Exercise, healthy eating, and sleep promote good health, yet mothers engage in low levels of these behaviours. This may be due to the guilt mothers feel when taking time away from their children to prioritize engagement in health-promoting behaviours. Self-compassion may decrease this guilt, however little is known about how mothers feel about engaging in the self-compassionate act of prioritizing health behaviours. The present study examined relationships between both self-compassion and fear of self-compassion with mothers' reactions to prioritizing health behaviours. Through an online survey, mothers rated their self-compassion, fear of self-compassion, and read a scenario about prioritizing the health behaviours of exercise, healthy eating, and getting adequate sleep. Next, mothers rated adjectives describing how they would perceive themselves if they behaved in the way the scenario described. Bivariate correlations revealed mothers high in self-compassion felt more positively and mothers low in self-compassion felt more negatively about prioritizing their needs for healthy behaviours. The current research provides insight into why some mothers feel better about incorporating health-promoting behaviours, such as exercise, into their lives than others.Acknowledgments: The funds for this project were provided from a University of Manitoba SSHRC Explore Grant.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".