Finding Ways to Lift Barriers to Care for Chronic Pain Patients: Outcomes of Using Internet-Based Self-Management Activities to Reduce Pain and Improve Quality of Life
Bibliographic record
Abstract
Background. Chronic pain is prevalent, disabling, costly, and undertreated. There is clearly a need to improve patient understanding of ways to manage their pain. Internet-based programs are continually being developed to facilitate mental health improvement, providing tailored content for patients to manage their pain, anxiety, and depression. Objective. To evaluate the impact of Internet-based patient self-management education and activities on patients' pain, anxiety, and quality of life in patients who could not access multidisciplinary pain management. Design. Observational study. Subjects. Two hundred (200) patients (61% females, 39% males, between 18 and 75 years old) from one community pain clinic in Toronto, Canada (Toronto Poly Clinic), participated. Patients had moderate to severe pain, depression, and anxiety. These patients committed to study from a group of 515 patients with chronic noncancer pain of different origins who were stable on their levels of pain, anxiety, and depression for 12 consecutive months before start of study and could not afford noninsured treatment modalities like physiotherapy, psychology, nutrition, or exercise therapy consultation. Methods. Patients were encouraged to visit two Internet sites (a blog and Twitter postings) for educational postings written by the author about exercise, nutrition, mindfulness meditation, disease management methods, evidence-based supplements, daily relaxation exercises, and overall self-management methods 15 minutes per day for six months. Patients were also encouraged to share their ideas and comments on a blog. Activity logs were kept by patients and reviewed by physician at follow-up visits. Compliance was encouraged via weekly email reminders and phone calls during the observation period. Results. Modest improvements were noted in pain, anxiety, depression, and quality of life. Of the patients with moderate or severe pain before treatment, 45% reported mild levels of pain after treatment, with a reduction of severe pain from 40% before treatment to 25% after treatment (p value 0.0184). Conclusion. Self-management support interventions, such as Internet-based educational tools, can be considered to help patients manage their chronic pain, depression, and anxiety and may be helpful to improve the treatment outcome in patients who could not otherwise afford noninsured services.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".