Self-care among military spouses and partners: Developing the Military and Veteran Spouse Self-Care Inventory (MVSSCI)
Bibliographic record
Abstract
Introduction: The need for a culturally appropriate, practical measure of self-care was identified during a peer support program evaluation conducted by the Institute of Military and Veteran Family Wellness at the University of Texas at Austin. The authors aimed to develop a concise version of the military-adapted 69-item self-care inventory (SCI) for use with military and Veteran spouses and partners. Methods: Military and Veteran spouses and partners completed the military-adapted SCI (N = 227). The data were then subjected to confirmatory factor analysis to reconfirm the Physical Self-Care, Psychological Self-Care, Emotional Self-Care, Spiritual Self-Care, and Professional Self-Care sub-scales. The resulting model was examined for criterion, discriminant, and convergent validity. Associations between the Military and Veteran Spouse Self-Care Inventory (MVSSCI) and generalized anxiety symptoms, depressive symptoms, perceived quality of life, and perceived social support were explored. Results: = 0.60). Discussion: The MVSSCI should be considered a reliable and valid measure of self-care practices across several life domains among military and Veteran spouses and partners. Limitations include that only one gender participated in this pilot study, and respondent fatigue led to missing data.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".