A Cross-sectional Study on Lower Back Pain amongst Medical Students and Foundation Doctors in Malta
Bibliographic record
Abstract
INTRODUCTION: Lower back pain (LBP) is one of the most common health problems and a significant global burden on both an individual and economic level. It is especially prevalent amongst healthcare workers and students, partly due to working conditions. This study aimed to investigate the prevalence and common risk factors of LBP amongst medical students and foundation doctors in Malta. METHODS: A mixed-methods cross-sectional, online questionnaire based on the Nordic Musculoskeletal Questionnaire was distributed to medical students and foundation doctors in Malta. Quantitative data was analyzed by means of Chi-squared test followed by multivariate analysis, whilst qualitative data was organized into broad themes. RESULTS: A total of 319 individuals participated in the questionnaire. The 12-month and 7-day prevalence of lower back pain in the sample population overall is 78.14% and 28.71% respectively. Females and students who worked during medical school were more likely to have LBP (p < 0.001 and p= 0.040 respectively). Long working/studying hours and awkward sitting/standing postures were identified as contributors to LBP by participants. CONCLUSIONS: Lower back pain is a highly prevalent issue amongst medical students and foundation doctors in Malta. If unaddressed, it will continue to contribute to disability, decreased quality of life and reduced career longevity. An approach based on Human Factors and Ergonomics principles focusing on ergonomic design and ergonomics training in medical school can lead to improved staff wellbeing and increase patient safety and efficiency.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".