Can Self‐Compassion Promote Healthcare Provider Well‐Being and Compassionate Care to Others? Results of a Systematic Review
Bibliographic record
Abstract
BACKGROUND: This meta-narrative review, conducted according to the RAMESES (Realist And Meta-narrative Evidence Syntheses: Evolving Standards) standards, critically examines the construct of self-compassion to determine if it is an accurate target variable to mitigate work-related stress and promote compassionate caregiving in healthcare providers. METHODS: PubMed, Medline, CINAHL, PsycINFO, and Web of Science databases were searched. Studies were coded as referring to: (1) conceptualisation of self-compassion; (2) measures of self-compassion; (3) self-compassion and affect; and (4) self-compassion interventions. A narrative approach was used to evaluate self-compassion as a paradigm. RESULTS: Sixty-nine studies were included. The construct of self-compassion in healthcare has significant limitations. Self-compassion has been related to the definition of compassion, but includes limited facets of compassion and adds elements of uncompassionate behavior. Empirical studies use the Self-Compassion Scale, which is criticised for its psychometric and theoretical validity. Therapeutic interventions purported to cultivate self-compassion may have a broader effect on general affective states. An alleged outcome of self-compassion is compassionate care; however, we found no studies that included patient reports on this primary outcome. CONCLUSION: We critically examine and delineate self-compassion in healthcare providers as a composite of common facets of self-care, healthy self-attitude, and self-awareness rather than a construct in and of itself.
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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 | 0.032 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".