THE FACTORS THAT CORRELATED WITH BACK PAIN IN PHYSIOTHERAPISTS
Bibliographic record
Abstract
Amac: Fizyoterapistlerde mesleki yuklenmelere bagli olarak bel agrisi siklikla gorulen bir durumdur. Bu calismanin amaci fizyoterapistlerde yas, vucut kitle indeksi, calisma posturu ve calisma yilinin bel agrisi ile ilgili ozurluluk duzeyi uzerine etkisini incelemektir. Yontem: Yirmi dokuz fizyoterapist calismaya dahil edildi. Calisma yili ve gunluk calisma saatleri kaydedildi. Olgularin bel agrisi ile ilgili sikâyetleri “Quebec Bel Agrisi Kisitlilik Olcegi” (QUEBEC) ile, calisma posturleri “Owako Calisma Posturu Analiz Sistemi” (OWAS) ile degerlendirildi. Bel agrisi ile yas, vucut kitle indeksi (VKI) , calisma posturu, calisma yili ve gunluk calisma saati arasindaki iliski Spearman korelasyon katsayisi kullanilarak incelendi. Bel agrisi olan ve olmayan fizyoterapistlerin yas, VKI, calisma yili, gunluk calisma saati ve calisma posturleri arasindaki fark Mann-Whitney U test ile degerlendirildi. Bulgular: QUEBEC skoru ile yas arasinda pozitif yonde orta derecede istatistiksel olarak anlamli iliski bulundu (r=0.44, p=0.01). Calisma yili ile calisma posturu (r=0.38, p=0.04) ve VKI (r=0.41, p=0.027) arasinda iliski oldugu goruldu. Bel agrisi olan ve olmayan fizyoterapistlerin yaslari istatistiksel olarak birbirinden farkli bulundu (p<0.05). Sonuclar: Calismamizda fizyoterapistlerde ilerleyen yasin bel agrisi ile ilgili ozurluluk duzeyini etkiledigi goruldu. Ayrica calisma yili arttikca calisma posturunun bozulmasi ve VKI’nin artmasinin bel agrisini tetikleyebilecek faktorler oldugu goruldu.
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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 | 0.003 |
| 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".