Prévalence et facteurs de risque des troubles musculosquelettiques chez les professionnels de la petite enfance
Bibliographic record
Abstract
Background: early childhood professionals are exposed to significant biomechanical and psychosocial constraints related to their work environment (e.g., lifting children, unsuitable furniture, stress). These constraints contribute to the development of musculoskeletal disorders (msds), particularly affecting the lower back, neck, and shoulders.Objective: to estimate the prevalence of msds among early childhood professionals and identify the main associated risk factors.Methods: a systematic review was conducted in accordance with prisma guidelines between august 15, 2024, and march 30, 2025. Four cross-sectional studies were selected from the pubmed and cochrane databases. The methodological quality of the studies was assessed using the modified newcastle-ottawa scale.Results: four cross-sectional studies were included. methodological quality varied across studies, ranging from moderate to good. A high prevalence of msds was observed, particularly in the lower back, neck, and shoulders. Several occupational risk factors were found to be statistically significant such as lifting children, floor-level postures, physical fatigue, as well as psychosocial factors like mental workload or lack of recognition.Discussion: this review highlights an association between certain occupational constraints and the occurrence of msds among early childhood professionals. However, methodological heterogeneity and potential biases limit the level of evidence and prevent any causal inference. Further studies are needed to confirm these findings.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".