Perceived physical stress at work and musculoskeletal discomfort in X-ray technologists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A structured questionnaire/interview was designed to explore demographic, personal, occupational and occupational health factors as well as recreational physical activities which can affect X-ray technologists' musculoskeletal symptoms. This questionnaire was piloted for clarity and validity. Subsequently, a random sample of 20 volunteer participants (18 female, 2 male) from two University hospitals were administered the questionnaire in the presence of the investigators to ensure that questions were correctly understood. The data obtained were analysed for magnitude, duration and frequency of activities and for severity, duration and recurrence of morbidity. The X-ray technologists in the sample were found to be a young group of professionals ranging from between 20 - 54 years of age. Eighty-nine per cent of the technologists were physically active and 44% indulged in physical recreational activities. Despite the young age and active life style, the X-ray technologists had significant and diverse musculoskeletal problems; 83% of technologists had backache and 39% of the female technologists had neck pain and 28% shoulder pain. The majority of technologists had suffered multiple episodes of pain. Fifty per cent of the female sample and both male volunteers suffered from upper extremity pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it