Kaulų-raumenų sistemos skausmų sąsajos su darbo aplinkos veiksniais intensyvios terapijos skyriaus slaugytojų tarpe
Bibliographic record
Abstract
\nObjective of the study. To establish the associations between musculoskeletal discomfort (pain) and occupational factors among general practice nurses at intensive therapy units.\nMaterial and methods. The questionnaire on anonymous base was performed: altogether, 120 questionnaires were given to intensive care nurses. Response rate reached 83% (n = 100). The questionnaire was composed using Dutch Musculoskeletal Questionnaire. Statistical data analysis was performed using Statistical Package for Social Sciences (SPSS 10.0).\nResults. The study revealed, that during last week prior to survey almost half of respondents felt discomfort in lower back area, neck pain was prevalent by 14%, ankle and feet pain by 8%, and upper back pain by 6% of nurses. More than half of respondents visited the doctor due to health problems during last 12 months. Our study showed, that more than half (60%) of nurses have not enough rest during the breaks. 54% of respondents indicated, that the unit has a lack of staff and this results in more intensive work during the shifts. 43% of nurses have overtime regularly. Lower back pain was caused by heavy load (in half of respondents) and uncomfortable position at work (in quarter of nurses). Lower back pain was prevalent in 84% of nurses, who participated at our survey. These women estimated it as an outcome of the work (OR = 7.7; p < 0.05). Our study also revealed, that neck and shoulders pain was caused by uncomfortable position at work (24%), climate... [to full text]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".