Exploring the Causes of Neck Pain and Disability as Perceived by Those Who Experience the Condition: A Mixed-Methods Study
Bibliographic record
Abstract
Designing effective treatment protocols for neck-related disability has proven difficult. Disability has been examined from structural, emotional, and cognitive perspectives, with evidence supporting a multidimensional nature. The patient’s perspective of their condition has found increasing value for patient-centred, evidence-informed care. This cross-sectional study utilized descriptive thematic analysis to examine perceptions of causation in 118 people with neck pain. The Brief Illness Perceptions Questionnaire was used to capture perceptions of causation for neck pain symptoms. The Neck Disability Index, the Pain Catastrophizing Scale, the Hospital Anxiety and Depression Scale, and the P4 pain intensity numeric rating scale were also collected. Eight main themes were found for the cause(s) of neck pain: posture and movement, structure and mechanism, emotions, predisposition and lifestyle, symptoms, fatigue and insomnia, treatment, and environment. A series of regression models stratified by perceived cause suggested that disability could be explained by different constructs across the larger of the main themes. The findings are discussed in terms of the false view that mechanical neck pain should be considered a homogenous condition and potential application to treatment decision making based on patient perspectives.
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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 | 0.034 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".