Postural Education Programmes with School Children: A Scoping Review
Bibliographic record
Abstract
Introduction: Spinal deformities and back pain have been a growing problem in childhood. Objetives: The present study undertakes a scoping review to identify scientific studies on school children’s postural education programmes, focusing on methodologies used, identifying the implementation key factors and gaps, and the results of those programmes.Methodology: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) was used in the scoping review. Five online databases were used to identify papers published since 2013. Eligibility criteria were defined, and the search strategies were drafted. Results: A total of 86 publications were initially identified, and after duplicates elimination, 45 papers remained. To increase the consistency, three researchers screened these 45 publications, and 34 papers were excluded after reading titles and abstracts. Therefore, the full texts of the 11 papers were analysed in detail for this study. The postural education programmes mainly focus on acquiring knowledge and the different teaching methodologies used. However, the few follow-up studies do not reveal consistent results regarding the maintenance of postural health competencies to effectively prevent pain and spine deformities in childhood and adolescence. Conclusions: This scoping review made it possible to analyse methodologies, key factors and gaps, and the outcomes of postural education programmes. From the results of this systematic review, a programme will be designed and applied to pre-school age children to evaluate the effects of improving postural control development and postural health promotion.
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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 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".