Increasing access to pain management: Feasibility of a self-compassion psychoeducational website using a minimally monitored delivery model
Bibliographic record
Abstract
Self-compassion has been associated with several positive pain-related outcomes. However, little is known about the impact of targeting self-compassion on pain management. This study assesses the feasibility of a self-compassion psychoeducation website among adults with chronic pain using a minimally monitored delivery model. Participants (N = 26) were recruited online and a single group pre-test and post-test design with a 3-month follow-up was used. The intervention was a 6-week program comprised of a video, writing exercises, guided meditations and automated emails. Feasibility outcome measures were grouped into the following categories: study engagement (ease of recruitment, attrition, adherence, satisfaction), pain vulnerability variables (intensity, interference, catastrophizing, mood) and protective pain variables (self-compassion, resilience and acceptance). Challenges pertaining to uptake were encountered. Attrition was higher (n = 11/26; 42%) and adherence to the full treatment protocol lower (n = 6/26; 23%) than expected. Treatment satisfaction was high with nearly all study completers (93%) reporting that they would recommend the program to a friend. Intent-to-treat mixed effects models showed a significant and large increase of self-compassion (d = 0.92) and a significant impact on several outcome variables (ds from 0.24 to 1.15) with most gains either maintained or increased at follow-up. The recruitment strategy may have negatively impacted participant engagement. Methodological modifications are proposed to improve the feasibility of the program. Minimally monitored web-based programs targeting self-compassion may benefit adults with chronic pain who may have limited access to traditional psychological services or who prefer online-based interventions.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".