Developing a Community-Based Exercise and Physical Programme for Runners With Patellofemoral Pain in Under-Resourced Communities: A Delphi Study
Bibliographic record
Abstract
Purpose: Patellofemoral pain (PFP) is a multifactorial condition that is highly prevalent among recreational runners. Poor homeostasis between load and runner's capacity is the main cause of PFP. A low-cost and community-based intervention is needed to address the rehabilitation needs of runners in under-resourced communities. The purpose of this study was to develop a community-based exercise and physical programme for runners with PFP in under-resourced communities. Method: This study used a Delphi method that included sports experts (physicians, physiotherapists, biokineticists, podiatrists, and sports therapists) who consented to participate. Delphi included three rounds of iterations to attain consensus among experts. Experts reached a consensus by rating PFP programme items using the 5-point Likert scale. Results: = 5). Delphi process yielded an exercise and physical intervention, which included the following 10 recommended strategies: hip muscle training, quadriceps muscle training, general lower limb and trunk strengthening, use of appropriate running shoes, and use of prefabricated in-shoe foot orthosis, tissue mobilization, patellar taping/bracing, education, flexibility exercises, addressing external loads, and addressing non-physical internal loads. Conclusions: A consensus was reached for a suitable exercise and physical programme for runners in under-resourced communities. A follow-up implementation study is, therefore, recommended.
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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 | 0.036 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".