Implementation of Physical Activity into routine Clinical pracTice in Rheumatic Musculoskeletal Disease: The IMPACT-RMD study protocol and rationale
Bibliographic record
Abstract
BACKGROUND: Physical activity is an important intervention for improving disease-related symptoms and systemic manifestations in rheumatic and musculoskeletal disease (RMDs). However, studies suggest that RMD patients report that the lack of individualized and consistent information about physical activity from managing doctors and healthcare professionals, acts as a barrier for engagement. On the other hand, managing doctors and healthcare professionals report lack of knowledge in this area and thus lack of confidence to educate and advise RMD patients about the beneficial effects of physical activity. The aim of the present study therefore, is to develop two e-Learning courses for RMD doctors and health professionals: a) the first one to provide consistent information about the collective benefits of physical activity in RMDs and b) the second on how to implement physical activity advice in routine clinical practice. METHODS: An international collaboration of seven countries, consisting of one academic institution and one patient organization from each country, will co-develop the two e-Learning courses. The final e-Learning courses will primarily target to improve - through physical activity advice - RMD symptoms which are important for patients. DISCUSSION: The main result of this study will be to co-develop two e-Learning courses that can be used by managing RMD doctors and healthcare professionals to be made aware of the overall benefits of physical activity in RMDs as well as how to implement physical activity advise within their practice.
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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 | 0.038 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".