Exploring the Relationship Between Compassion and Attachment in Individuals with Mental Health Difficulties: A Systematic Review
Bibliographic record
Abstract
Abstract The objective of the review was to explore the relevance of the relationship of compassion and attachment to mental health. APAPsycInfo, APAPsycArticles, CINAHL, MEDLINE, Social Science Database, Sociology Database, PTSDpubs, Pubmed, and Web of Science were searched from their inception until November 9, 2021. Peer-reviewed empirical studies exploring the compassion–attachment relationship in individuals with mental health difficulties through outcome measures were included. Studies were excluded if non-empirical, with non-clinical/subclinical samples, in a language other than English and if they did not consider the compassion–attachment relationship. Risk of bias was assessed through The Newcastle–Ottawa Scale and the Downs and Black Checklist. Seven eligible studies comprising 4839 participants were identified, with low to moderate risk of overall bias. Findings indicated a more straightforward relationship between self-compassion and secure attachment and confirmed the relevance of compassion and attachment to psychological functioning. Limitations concerned study design, the use of self-report measures, and low generalisability. While suggesting mechanisms underpinning compassion and attachment, the review corroborates the role of secure attachment and self-compassion as therapeutic targets against mental health difficulties. This study is registered on PROSPERO number CRD42021296279.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".