Prevalence of and Risk Factors Associated with Neck Problems in Undergraduate Physiotherapy Students
Bibliographic record
Abstract
Purpose: To determine the point prevalence of and risk factors associated with neck problems in undergraduate physiotherapy students. Method: In March 2001, all undergraduate students enrolled in the bachelor of physiotherapy program at the University of South Australia were invited to participate in this study. Students completed a reliable and valid questionnaire over four recall periods, eliciting information as to the prevalence of neck problems and possible risk factors (environmental factors and students' demographic characteristics). Results: Two hundred fifty students (response rate = 72%) participated. The prevalence of neck problems was 61% over the lifetime, 55% over the previous year, 38% over the previous month, and 32% over the previous week. The point prevalence increased substantially between the first and second years and continued at this higher level during the fourth year. Second- and fourth-year students were approximately twice as likely to experience neck problems compared with first-year students. Being female and exposure to multiple occupational activities for long periods of time were significantly associated with neck problems. Study and stress were also contributing factors. Conclusions: Students and lecturers should attend to the risk factors associated with developing neck problems in undergraduate physiotherapy students. Addressing these risk factors could possibly lead to a reduction in neck problems later in practice.
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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 | 0.000 | 0.004 |
| 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 | 0.001 | 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".