Self-Compassion Among Youth with Child Maltreatment Histories and Psychological Distress: A Scoping Review
Bibliographic record
Abstract
Objective: Focusing on youth (ages 15-24), our scoping review aims to address these questions: (1) What is the relationship between self-compassion (SC) and psychological distress in youths with child maltreatment (CM) histories? and (2) How does this relationship differ across child maltreatment types?Methods: Eight databases were screened: OVID MEDLINE, OVID PsychInfo, PsycARTICLES, ProQuest Sociological Abstracts, ProQuest ERIC, OVID Embase, CINAHL, and PUBMED. Our search strategy and inclusion/exclusion criteria yielded an initial 4143 studies. With 1365 duplicates removed, 2778 titles and abstracts were screened. 17 studies were included for full-text screening, and seven studies were selected for data extraction and final inclusion.Results: SC was found to moderate and mediate the relationships between CM and psychological distress. The role of fear of SC was also investigated and found to function as a mediator between CM and PTSD symptom severity. Regarding CM types, emotional abuse was found to significantly predict SC levels in a child welfare population.Implications: Given the significance of SC and fear of SC in the relationship between CM and psychological distress, implementation of SC into clinical practice should be considered. Recommendations are made to expand research into more diverse populations, such as child welfare and/or Indigenous youth.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".