Ten Footsteps 2021. An Online course to support self-management of pain
Bibliographic record
Abstract
ObjectiveTo describe an online self-management course that was piloted as part of Footsteps 2021, an online festival provided on a voluntary basis by a team of health care professionals and experts by experience with the intention of supporting people to live well with pain.Design A feasibility study to assess who would engage with the course and potential areas of benefit. The Ten Footsteps Course mirrors a multidisciplinary pain management programme and was offered to anybody who identified that they would find it helpful to attend the sessions.ResultsA pre-course survey indicated that people who signed up were familiar with the idea of self-management and were largely in alignment with its ethos. Scores completed on a range of questionnaires were in alignment with those reported from formal pain management services. Thirty-two people completed the pre-course questionnaire. Only nine people completed a follow-up questionnaire at the end of the course. At the end of the course average reported levels of self-efficacy were higher and catastrophizing were lower compared to at the beginning while pain intensity and overall wellbeing did not differ at the two timepoints.ConclusionsPain self-management support co-produced and delivered in an open access format is of interest to people living with pain and some appear to benefit. There is a need to explore how this intervention can be made more accessible, including the use of different formats of delivery.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".