Early Nurturing Experiences, Self-Compassion, Hyperarousal and Scleroderma The Way We Relate to Ourselves May Determine Disease Progression
Bibliographic record
Abstract
Scleroderma is a rare, painful and complex autoimmune connective tissue disease that can lead to death. The physiology of symptom onset and progression and the psychological aspects of living with this chronic disease have been studied fairly extensively. However, there is limited knowledge about scleroderma and negative physiological arousal (hyper-arousal: linked to immune dysfunction resulting in autoimmunity in the face of stressful events) and how levels of hyper-arousal are related to stress experienced at an early age; to emotion regulation coping strategies such as self-compassion; and to when scleroderma is experienced (earlier or later onset). Knowledge about these relationships may be important information for the treatment of scleroderma and related illnesses. This study addressed these relationships for scleroderma by examining how hyper-arousal was linked to these psychosocial experiences of stress, to coping strategies, and to age of disease onset. A within group design was utilized. A total of 122 participants were recruited from Australia (39) the United Kingdom (81) and country not specified (2) and invited to complete an online or a hard copy survey. Lower positive early life experiences, lower levels of self-compassion and an earlier onset of disease were all related to elevated levels of hyper-arousal in individuals diagnosed with scleroderma. A regression equation showed all three contributed significantly to the experienced hyper-arousal. The findings suggest that greater self-compassion may be a determining factor in how earlier emotional experiences are managed and in predicting lower hyper-arousal in terms of this disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".