Postural Education Programmes with School Children: A Scoping Review
Bibliographic record
Abstract
Spinal deformities and back pain are growing problems in childhood and adolescence, due to unhealthy habits. This study undertook a scoping review to identify scientific studies with children and adolescents, focusing on the methodologies used, implementation of key factors and gaps, and results of postural education programmes to promote sustainable healthy habits. The methodological tool PRISMA-ScR was used. Five online databases were used to identify papers published since 2013. Eligibility criteria were defined, and the search strategies were drafted. A total of 86 publications were initially identified; after screening and applying eligibility criteria, 11 papers were included in this study for detailed analysis. The postural education programmes in these papers mainly focused on adolescents’ postures and postural learning acquisition, using different teaching methodologies; only one study was conducted with children between 5 and 6 years old enrolled in preschool. Follow-up studies revealed inconsistent results. However, developing and measuring the effectiveness of young children’s postural education programmes, to enhance experiences of movement variability and strategies for postural control in playful activities, is of great relevance for children’s healthy development, and can also have positive impacts on environmental and social sustainability by promoting healthy and conscious lifestyles.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".