Can Self‐Compassion Promote Healthcare Provider Well‐Being and Compassionate Care to Others? Results of a Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it