Replication: Self-Compassion and Health-Promoting Lifestyle Behaviours in Albertan University Students During the COVID-19 Pandemic
Bibliographic record
Abstract
Self-compassion as a predictor for health-promoting behaviours has been the subject of several research studies. Self-kindness, common humanity, and mindfulness have been repeatedly positively correlated with health-promoting behaviours in individuals, such as eating well and doing physical activity (Gedik, 2019; Holden et al., 2020). We hypothesized that the positive components of self-compassion (self-kindness, common humanity, and mindfulness) would positively correlate with health-promoting behaviours. In an attempt to replicate Gedik’s (2019) study, researchers recruited 294 Albertan post-secondary students to respond to an online-based questionnaire. Participants filled out both the Self-Compassion Scale (SCS) (Neff, 2003b) and the Health Promoting Lifestyle Profile II (Walker et al., 1995). Findings revealed that, unlike Gedik (2019), isolating behaviours such as feeling cut-off from the world are indicative of improved stress management. Therefore, Gedik’s (2019) results were not replicated. This research’s implications are essential when considering the factorial breakdown of self-compassion and how the factorial relationships to health behaviours are affected by varying populations and contexts. Specifically, the occurrence of the COVID-19 pandemic and its resulting restrictions must be considered when interpreting the results presented in this current study.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".