Evaluating the reliability and validity of the Self-Compassion Scale adapted for children
Bibliographic record
Abstract
This study introduces the Self-Compassion Scale adapted for Children (SCS-C) and presents psychometric findings regarding its reliability and validity. A sample of 382 students in 4th to 7th grade provided data on the SCS-C and measures of mindfulness, self-concept, indicators of well-being, empathic-related responding, and prosocial goals. Teachers provided data on students’ social and emotional competence and empathy/sympathy. Results indicated a two-factor structure for the SCS-C with negatively-worded items and positively-worded items forming two discrete subscales each with high internal consistency. As predicted, students’ scores on the SCS-C were significantly related to multiple indicators of social and emotional well-being, demonstrating preliminary evidence of convergent validity. In addition, scores on the SCS-C were found to differ across grade level, with students in 5th grade reporting higher scores on the SCS-C than students in 4th grade and students in 6th grade. This study provides insight into the factor structure of the SCS-C, as well as the relations of self-compassion to other indicators of social and emotional well-being in childhood and pre-adolescence. Limitations and future directions are discussed with regard to the relevance of the SCS-C for research and applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.025 |
| 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 | 0.001 |
| 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".