Use of a brief version of the self-compassion inventory with an international sample of people with HIV/AIDS
Bibliographic record
Abstract
The objective of this study was to extend the psychometric evaluation of a brief version of the Self-Compassion Scale (SCS). A secondary analysis of data from an international sample of 1967 English-speaking persons living with HIV disease was used to examine the factor structure, and reliability of the 12-item Brief Version Self-Compassion Inventory (BVSCI). A Maximum Likelihood factor analysis and Oblimin with Kaiser Normalization confirmed a two-factor solution, accounting for 42.58% of the variance. The BVSCI supported acceptable internal consistencies, with 0.714 for the total scale and 0.822 for Factor I and 0.774 for Factor II. Factor I (lower self-compassion) demonstrated strongly positive correlations with measures of anxiety and depression, while Factor II (high self-compassion) was inversely correlated with the measures. No significant differences were found in the BVSCI scores for gender, age, or having children. Levels of self-compassion were significantly higher in persons with HIV disease and other physical and psychological health conditions. The scale shows promise for the assessment of self-compassion in persons with HIV without taxing participants, and may prove essential in investigating future research aimed at examining correlates of self-compassion, as well as providing data for tailoring self-compassion interventions for persons with HIV.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".