e-Pain: technology-based innovations for the study of pain
Bibliographic record
Abstract
Aim: The symposium will explore: • Features of e-pain technologies • Challenges in developing e-pain technologies • Existing evidence in relation to technology-based pain management • Debate regarding regulation of e-pain treatments • Likely steps for future development of these interventions. Rationale Traditional approaches to interventions for chronic pain are subject to numerous constraints, including direct and indirect costs, highly labour intensive, long waiting lists, mobility and accessibility issues, and shortages in appropriately trained health care professionals. To address these obstacles, researchers have begun to administer psychological interventions via various technologies. The findings from the studies discussed in the symposium will assist patients and researchers to make informed decisions regarding which modalities deliver more effective interventions for chronic pain. The possibilities and challenges facing such interventions will be discussed in detail. Summary Paper 1 (B. McGuire) sets the scene in terms of describing the move towards technology-based treatments and some of the challenges in reaching a wide audience. Suggestions are made for the involvement of health professionals, patients and software experts to ensure effectiveness, relevance, quality and adherence. In paper 2, (J. Stinson), the development of a successful smart-phone intervention for children with chronic pain is described. Paper 3 (S. O’Higgins) describes the evaluation of an online intervention and illustrates some of the problems and frustrations with rolling out a randomised trial. Paper 4 (J. Egan) describes the qualitative process involved in determining the need for cultural adaptation of Canadian and American materials for the European context. In session 5, we will show a video of an intervention for teenagers that exemplifies what can be done when researchers collaborate with experts in software development. In the final session, we will facilitate a discussion so that participants can consider whether e-pain interventions are desirable and feasible in their own health-care settings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".