Les systèmes d'orientation à l'activité physique au Royaume-Uni : efficacité et enseignements
Bibliographic record
Abstract
INTRODUCTION: UK Exercise Referral Systems (ERS) have been developed to encourage physical activity in the general population. This systematic review investigated the effectiveness and cost-effectiveness of ERS. Identification of factors influencing ERS uptake, adherence and success were also investigated. METHODS: Studies were identified from Medline, Cochrane and Pascal and bibliographies of relevant papers. Interventions providing access to ERS (randomized controlled trials or controlled trials), experimental or qualitative studies, and meta-analyses were included. RESULTS: Twenty six studies met the inclusion criteria. Compared with usual care, ERS showed an increased number of participants who achieved 90-150 minutes of physical activity of at least moderate intensity per week. However, no significant difference in long-term outcomes (e.g., quality of life, body mass index, glycated haemoglobin, anxiety) were identified between ERS and comparator groups. Cost-effectiveness analysis suggested that ERS were more cost-effective for participants with co-morbid medical conditions. A higher adherence rate was associated with better effectiveness of ERS. DISCUSSION: Limited evidence supports the efficacy of ERS to increase physical activity or improve health outcomes. This evidence-based analysis could support the development of effective ERS in France.
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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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".