Actively Waiting: Feasibility and Acceptability of a Virtual Self-Management Program Designed to Empower People With Chronic Pain Waiting for Interprofessional Care
Bibliographic record
Abstract
Chronic pain affects 1.9 billion people worldwide and wait times for interprofessional pain management programs can be extensive. The existing wait times provide an opportunity to introduce internet-based interventions that enhance self-management ability. PURPOSE: The purpose of this study was to examine the feasibility, acceptability, engagement, and meaningfulness of an online program designed to enhance the readiness for change and self-management. DESIGN: Participants (N = 61) waiting for interprofessional chronic pain care at two centers were assigned to engage in either a series of eight self-directed web-based modules or engage in the modules with the addition of four one-on-one sessions delivered by coaches trained in motivational interviewing techniques. METHODS: We collected participant demographics, feasibility and engagement metrics, and pre and post intervention questionnaires. A subset of participants from each group participated in an interview (n = 22). RESULTS: The use of online modules was found to be feasible and acceptable for participants and engagement varied depending on individual preference and between modules. Participants noted that the content and approach were relevant and meaningful, influencing changes in thinking and behaviour around pain self-management. Exploratory analyses were performed and supported improvement in self-efficacy and chronic pain acceptance outcomes in both groups. Coaching did not augment improvements in any of our outcomes. CONCLUSIONS AND CLINICAL IMPLICATIONS: The use of a self-directed web-based chronic pain and motivational empowerment program appears to be a promising option to support people waiting for specialist care and may influence readiness for interprofessional care.
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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 | 0.005 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".