Exploring the emotional experiences of women with chronic pain: The potential role of self-compassion
Bibliographic record
Abstract
Chronic pain is a multidimensional phenomenon that can take an enormous toll on people's lives. In addition to coping with the physical pain, individuals experience the emotional dimension of chronic pain, which consists of the painful emotions directed towards the long-term implications of having chronic pain. People often engage in isolation and avoidance behaviours to manage chronic pain. Recently, there has been a shift away from avoidance and gravitation toward coping through acceptance and awareness of difficult emotions. Self-compassion (SC), being touched by one's own suffering and not disconnecting from it, may be useful in regulating difficult emotional chronic pain experiences. The purpose of this narrative study was to explore the role of SC in the emotional experiences of women with chronic pain. Three women with chronic pain participated in a focus group, reflexive photography, and one-on-one interviews. The data were analyzed using a holistic-content mode of analysis, generating narratives across the women's experiences. The narratives suggest that the women's emotional experiences of chronic pain are rooted in frustration, which stems from: (a) a lack of answers about the cause of their pain, (b) having no control over the pain, and (c) being limited in daily activities. The narratives further suggest that SC may help the women to: (a) put emotions into perspective, (b) be aware of their needs, and (c) realize that the pain is not their fault. Teaching women with chronic pain to be self-compassionate may be a useful strategy to create constructive perspectives of chronic pain.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".