Posture and Pain: Beliefs and Attitudes of Patients With Chronic Low Back Pain
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Posture is often associated with pain by patients, professionals, and health information channels. However, the extent to which patients perceive the relationship between posture and chronic pain is not well understood. This study aimed to investigate and understand the beliefs and attitudes related to posture among patients with chronic low back pain. METHODS: This is a qualitative descriptive study that investigated individuals with chronic low back pain, who were on the waiting list for physiotherapy. Data were collected through individual and semi-structured interviews. Thematic content analysis was used to analyse the data. RESULTS: Fifteen adults (11 women and 4 men) were interviewed. Three themes were identified and mapped within the dimensions proposed by the Common Sense Model: (1) Identity, (2) Cause and (3) Control. Participants' statements about posture predominantly followed the biomedical model, with participants holding a mental representation of a 'correct' posture necessary for maintaining a healthy spine. They associated perceived incorrect postures or positions with the cause or worsening of pain. To control symptoms, participants believed that constant care and monitoring of posture in various situations were necessary. CONCLUSION: Misconceptions about ideal posture and its relationship to chronic pain can lead individuals with low back pain to engage in constant monitoring and avoid certain movements and positions in daily activities. These beliefs may negatively impact prognosis, contribute to the maintenance of symptoms, and affect adherence to treatments.
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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 | 0.002 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".