Prioritizing chronic pain self-management amid coexisting chronic illnesses: An exploratory qualitative study
Bibliographic record
Abstract
Background: In Canada, one out of five people lives with chronic pain, a condition frequently co-occurring with other chronic illnesses. As with most chronic illnesses, successful engagement in symptom management is key. In the context of multiple illnesses, self-management involves daily prioritization of symptoms and conditions and decision-making, which can be challenging. Self-management of chronic illnesses can require more complex competence and tasks to address the different implications of each condition. Objective: Our research objective was to explore types and processes of self-management symptom prioritization among adults living with chronic pain and other chronic illnesses. Design: This research was carried out as part of a larger study that adopted an explanatory sequential mixed-methods design. This study focused more specifically on the qualitative part of the study. Settings: Participants recruited for the qualitative component took part in a semi-structured individual interview online or in-person at the center hospitalier de l'Université de Montréal. Participants: In total, 25 participants were interviewed, including 18 women and 7 men. Methods: To participate in the qualitative part of the study, participants were selected from the larger study and were eligible if they were 18 years old or older and experiencing pain for more than 3 months and had at least one other chronic illness for which they were receiving treatment or engaged in symptom management. Semi-structured interviews were conducted in-person or virtually and were transcribed verbatim. Reflexive thematic analysis was used to explore patients' narratives, and an open and iterative approach was adopted to code interviews and generate themes. Findings: The first theme, focus on symptom prioritization, showed different prioritization processes, including prioritizing a dominant illness, prioritizing multiple illnesses to avoid undesirable consequences, and finally absence of or automatic processes of prioritization. In the second theme, we identified several characteristics of an illness, in this case chronic pain that made it a self-management priority: uncontrollable and disabling nature, omnipresence, unpredictability, unpleasantness, and invisibility to others. In the last theme, we highlighted that some psychosocial factors influenced levels of engagement in self-management and prioritization processes, including social support and the patient-physician relationship. Conclusions: Chronic pain was the medical condition most often prioritized by participants in their self-management tasks. Because of its characteristics, it was the medical condition that had the most negative impact on day-to-day functioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".