An online training resource for clinicians to optimise exercise prescription for persistent low back pain: Design, development and usability testing
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is the leading cause of disability worldwide. A recent realist review identified the behavioural mechanisms of trust, motivation, and confidence as key to optimising exercise prescription for persistent LBP. OBJECTIVES: Our objectives were to (1) design and develop an online training programme, and (2) gain end-user feedback on the useability, usefulness, informativeness and confidence in using the online training programme using a mixed-methods, pre-post study design. PARTICIPANTS AND INTERVENTION: The online training programme was designed and developed using the results from a realist review, and input from a multi-disciplinary stakeholder group. A five-module online training programme was piloted by the first 10 respondents who provided feedback on the course. Further modifications were made prior to additional piloting. The satisfaction, usefulness, ease of use, and confidence of clinicians in applying the learned principles were assessed on completion. RESULTS: The online programme was advertised to clinicians using social media. Forty-four respondents expressed initial interest, of which 22 enrolled and 18 completed the course. Of the participants, most were physiotherapists (n = 16/18, 88.9%), aged between 30 and 49 (n = 11/18, 61.1%). All participants were satisfied with the course content, rated the course platform as easy to use and useful, and reported that they were very confident to apply the learning. Most (n = 10/14, 71.4%) reported that their manner of prescribing exercise had changed after completion of the course. CONCLUSIONS: An online training programme to optimise exercise prescription for persistent LBP appears to be easy to use, informative and improves confidence to apply the learning.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".