Evidence map›Paper›PMID 36313995›Full record

ArticleFrontiers in medicine2022

How does self-compassion help people adjust to chronic skin conditions? A template analysis study.

Elaine N Clarke, Paul Norman, Andrew R Thompson

Abstract read
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Elaine N ClarkeCentre for Behavioural Science and Applied Psychology, Sheffield Hallam University, Sheffield, United Kingdom.
Paul NormanDepartment of Psychology, University of Sheffield, Sheffield, United Kingdom.
Andrew R ThompsonDepartment of Psychology, University of Sheffield, Sheffield, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Skin conditions can greatly impact people's lives, but greater understanding of the processes involved in positive adjustment is required. Self-compassion has strong links to wellbeing and adaptive functioning and therefore may play an important role in adjustment to skin conditions. Design: Template analysis was used to explore how self-compassion operates in people living with skin conditions, with reference to existing theories of self-compassion. Methods: Semi-structured interviews were conducted with highly self-compassionate people with chronic skin conditions ( Results: Participants reported a variety of ongoing skin-related difficulties and their ways of managing these. Sensitivity to distress and care for wellbeing were identified as foundation themes: necessary components of a compassionate response to distress. Eleven types of difficulty-management strategies built upon these foundation themes: empathy, non-judgement, distress tolerance, self-kindness, mindful attention, perspective-taking, self-talk, self-care, using social support, concealment, and idiosyncratic coping strategies. Conclusions: Components of self-compassion helped people adjust to chronic skin conditions in a wide variety of ways, indicating that psychological adjustment is not a simple, linear process. Sometimes compassionate responses occurred automatically and sometimes with deliberate effort. Further research on compassion-based interventions for people with skin conditions is warranted.

Indexed as

adjustmentqualitativeself-compassionskin conditionstemplate analysis

Identifiers

PMID36313995
PMCPMC9606614

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.