Existential therapy for treating chronic pain: A scoping review
Bibliographic record
Abstract
Integrating an existential lens into chronic pain treatment enables a holistic and biopsychosocial approach to treating chronic pain. This scoping review aimed to identify the characteristics, themes, and results of existential therapy for treating chronic pain. We searched seven databases for empirical papers that described an existentially based intervention for a chronic pain sample. Conducted in May 2023, the search terms were related to chronic pain and truncated versions of existential and humanistic therapies. Six articles met the criteria and were included. Quantitative and qualitative studies were included, ranging from randomized controlled trials, non-randomized experiments, a case report, and a case study. Extracted data included study characteristics and common intervention themes (e.g., meaningfulness, self-identity, etc.) and outcome themes (e.g., pain-related, life satisfaction, etc.). All studies reported a reduction in pain symptoms or severity. Physical functioning was generally not influenced by existential therapy; however, it did improve with or in combination with cognitive-behavioral therapy (CBT). Meaningfulness was a common theme in all the interventions, and most articles reported increased meaningfulness in the outcomes. Spirituality seems to moderate the effectiveness of existential therapy. This offers an overview of the current state of knowledge of academically labeled existential therapies for chronic pain treatment. This further provides suggestions for advancing the study of this topic by highlighting both commonalities and inconsistencies in intervention findings.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".