The suffering of chronic pain patients on a wait list: Are they amenable to narrative therapy?
Bibliographic record
Abstract
Background: Chronic pain affects one in five Canadians. People with chronic pain frequently experience loss in their lives related to work, relationships, and their independence. They may be referred to a chronic pain program, which aims to strengthen coping through medical intervention and self-management skills. Data suggest that, even when individuals begin their pain program, many feel overwhelmed and do not continue.Aims: The aim of this study was to conduct a needs assessment to explore the acceptability and feasibility of developing a psychosocial intervention, narrative therapy (NT), to address loss for chronic pain patients on the wait list of a chronic pain program.Methods: Two focus groups were conducted with ten patients who had experienced being on a wait list for a provincial chronic pain management program (CPMP). Transcribed interviews were subjected to thematic and interpretive analysis.Results: Two major themes emerged from the analysis: loss of identity and sharing a story of chronic pain. All patients were enthusiastic toward an NT intervention, although individual preferences differed regarding mode of delivery.Conclusions: Loss is a significant part of the chronic pain experience. NT seems to be an acceptable intervention to address loss for patients on the wait list for a chronic pain program.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".