Perceived physical stress at work and musculoskeletal discomfort in X-ray technologists
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.
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 | 0.005 |
| 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) | 0.002 | 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".