Psychometric validation of the Self-Compassion Scale: Short Form in two Canadian Armed Forces samples
Bibliographic record
Abstract
Introduction: Self-compassion, the tendency to have positive self-regard even in the face of failure and weakness, is an important protective factor for mental health in military personnel and Veterans. However, the primary shortened self-report measure, the Self-Compassion Scale — Short Form (SCS-SF), has not been validated in military samples. Method: In the current study, we evaluate the factor structure of the SCS-SF with a sample of 807 Canadian military members, confirm the factor structure with a second sample of 525 Canadian military members, and test the differential correlations of developed SCS-SF Self-Kindness and Self-Criticalness sub-scales with mental health and coping variables. Results: In each military sample, the two-factor confirmatory factor analysis solution with self-kindness and self-criticalness as factors had the best fit and factor interpretability when Item 7 was not included in the analyses. Each factor had distinct associations with external variables, providing further convergent and discriminant support that each sub-scale represents different constructs. Discussion: Future use of the SCS-SF and, in particular, its sub-scales of self-kindness and self-criticalness in military samples is encouraged.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".