Understanding the chronic pain journey and coping strategies that patients use to manage their chronic pain: a qualitative, patient-led, Canadian study
Bibliographic record
Abstract
OBJECTIVE: To gain an insight into coping strategies that people living with chronic pain use to self-manage their pain. DESIGN: This qualitative Patient-oriented Research study used the Patient and Community Engagement Research approach. It was conducted by people with chronic pain lived experience, ensuring that patient perspective and needs were considered and addressed throughout the research cycle. Purposeful sampling was used for recruiting individuals living with chronic pain. A focus group and one-on-one semi-structured interviews were conducted via videoconference. The data were analysed iteratively using inductive thematic analysis and narrative story analysis. SETTING: Calgary, Alberta, Canada. PARTICIPANTS: Eleven adult participants, between the ages of 18 and 65, who self-identified as living with chronic pain for greater than 2 years. RESULTS: Three main themes emerged from the data: (1) the elements of chronic pain, (2) the chronic pain journey to acceptance and (3) daily coping strategies for chronic pain. Participants thought it was important to discuss these three themes because the daily coping strategies that they employed at any given time (theme 3) depended on the factors discussed in themes 1 and 2. Overlaying all of this is also a grieving process that people living with chronic pain may have to go through more than once. Participants also identified recommendations for healthcare professionals to support people living with chronic pain. CONCLUSIONS: Dealing with chronic pain affects all aspects of a person's life and involves a grieving process. When treating patients with chronic pain, it is important for healthcare professionals to understand the journey that people living with chronic pain go through, not just coping strategies. Diagnosis is critical for a patient's acceptance and in helping them find their new normal where they can employ daily coping strategies to manage their 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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".