Effectiveness, acceptability and feasibility of an Internet-delivered cognitive behavioral pain management program in a routine online therapy clinic in Canada
Bibliographic record
Abstract
BACKGROUND: Access to face-to-face cognitive behavioral pain management programs is very limited. Internet-delivered cognitive behavioral pain management has potential to improve client access to care but is not readily available in Canada. AIMS: The present study explored the effectiveness, acceptability, and feasibility of a previously validated Internet-delivered cognitive behavioral pain management course, the Pain Course, when offered in a publicly funded provincial Online Therapy Clinic. The five-lesson course was delivered over 8 weeks and was accompanied by brief weekly contact from a coach via weekly telephone calls and secure online messages. METHODS: = 55). Effectiveness was assessed by examining symptom measures at pretreatment, posttreatment, and 3-month follow-up. Completion rates and satisfaction ratings were used to examine acceptability. Feasibility was assessed by examining time required for service delivery. RESULTS: = 0.52; 32% reduction) at posttreatment that were maintained at follow-up. Completion rates (76%) and course satisfaction ratings (85% would recommend course) were high. Coach time per week was estimated as M = 12.67 (SD = 6.53) min. CONCLUSIONS: The findings add to existing literature on the Pain Course demonstrating for the first time the effectiveness, acceptability, and feasibility of Internet-delivered cognitive behavioral pain management programs for adults with chronic pain in a routine online therapy clinic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".