Comparison of Physical Literacy and Upper Extremity Functions in Girls With and Without Upper Crossed Syndrome
Bibliographic record
Abstract
Introduction: Upper crossed syndrome causes movement limitation and weakness in people's movement functions. This study aimed to compare physical literacy and upper extremity movement functions in girls with and without the upper crossed syndrome.Methods: The method of this research was descriptive and causal-comparative using field data collection. The statistical population of the research included two groups of healthy 10- to 12-year-old female students and also students with the upper crossed syndrome in Khalkhal City in the academic year of 2022-2023. Using G-power software, the statistical sample size was determined to be 60 people. These people were screened using a checkerboard and after quantitative height assessment, they were assigned to two Healthy (30 people) and the Upper Cross Syndrome (30 people) groups. Forward head angle and forward shoulder angle were measured using photography, kyphosis angle was measured using the Goniometer-pro app. The Canadian Assessment of Physical Literacy – version 2 (CAPL-2) questionnaire was used for the evaluation of physical literacy and Upper Quarter Y-Balance Test (UQYBT) was used to assess upper limb function. The Data were analyzed using an independent t-test in SPSS-26 software at the significant level of 0.05.Results The results showed that healthy girls were significantly in a better condition in all variables of physical literacy and upper limb function than girls with the upper crossed syndrome.Conclusion: According to the results of this research, it is suggested that coaches and sports and health professionals use the results of this study to develop the physical literacy and functions of students with the upper crossed syndrome.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".