Conceptualization, use, and outcomes associated with compassion in the care of people with multiple sclerosis: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Compassion is widely regarded as an important component of high-quality healthcare. However, its conceptualization, use, and associated outcomes in the care of people with multiple sclerosis (PwMS) have not been synthesized. The aim of this review is to scope the peer reviewed academic literature on the conceptualization, use, and outcomes associated with compassion in the care of PwMS. METHODS: Studies were eligible for inclusion if reporting primary research data from quantitative, qualitative, or mixed-methods studies on the conceptualization, use, and outcomes associated with compassion in the care of PwMS. Relevant studies were identified through searching five electronic databases (CINAHL, Cochrane Library, EMBASE, MEDLINE, and PsycINFO) in January 2022. We followed the guidance outlined in the Joanna Briggs Institute (JBI) manual for evidence synthesis, and also referred to the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews Checklist (PRISMA-ScR). Simple descriptive methods were used to chart quantitative findings, and a descriptive approach with basic content analysis was employed to describe qualitative findings. RESULTS: Fifteen studies were included (participant n = 1722): eight quantitative, six mixed-methods, one exclusively qualitative. Synthesized qualitative data revealed that PwMS conceptualize compassion as involving self-kindness, agency, and acceptance. PwMS report using self-compassion in response to unpleasant sensations and experiences. Quantitative findings suggest that compassion may mediate benefit finding, reduced distress, and improved quality of life (QoL) in PwMS, that those with the condition may become more compassionate through time, and that self-compassion specifically can be increased through training in mindfulness. In this context, greater self-compassion in PwMS correlates with less depression and fatigue, better resilience and QoL. Among studies, self-compassion was the most common outcome measure for PwMS. CONCLUSIONS: A nascent literature exists on the conceptualization, use, and outcomes associated with compassion in the care of PwMS. Further research is required to better understand what compassion means to PwMS and those caring for them. However, self-compassion can be cultivated among PwMS and may be helpful for managing unpleasant somatic symptoms and in benefit finding. Impact on other health outcomes is less clear. The use of compassion by health care providers in the care of PwMS is unstudied.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".