Reproducibility of a task description questionnaire for working pregnant women
Bibliographic record
Abstract
The objective of this study was to evaluate the reproducibility of a Task Description Questionnaire that was designed to investigate exposures to, and influential factors for, problematic tasks experienced by working pregnant women. The questionnaire comprised questions concerning 22 task components (covering working posture, manual material handling, work pace, prolonged postures and others), eight influential factors contributing to problematic tasks, discomfort (measured using a body map) and level of effort to perform the tasks. Reproducibility of the questionnaire was assessed by interviewing participants on two occasions one week apart for interviews at both 20 and 34 weeks of pregnancy. Eleven and 13 problematic tasks were reported by 21 working pregnant women at 20 and 34 weeks of pregnancy, respectively. These tasks were surveyed using the Task Description Questionnaire. Kappa statistics and correlation coefficients (supplemented by paired t-tests) were used to examine the reproducibility of responses to the questionnaire. The results showed that most of the variables were measured with very good or satisfactory reproducibility. The reproducibility of exposure to work posture was higher than that of exposure to manual material handling. There was no significant difference between test and retest means for the discomfort scores measured on the body map, except for the maximum discomfort score for the whole body in the 34 weeks survey. The study suggests that the questionnaire can be reliably used in the study of problematic tasks experienced by pregnant women. But an initial preview of the questions by the subjects and explanation of the questions given to the subjects by the interviewer may help to produce more reliable results.
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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 | 0.037 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".