What do the general public believe about the causes, prognosis and best management strategies for low back pain? A cross-sectional study
Bibliographic record
Abstract
Abstract Background Low back pain (LBP) is one of the most common reasons for seeking health care and is costly to the health care system. Recent evidence has shown that LBP care provided by many providers is divergent from guidelines and one reason may be patient’s beliefs and expectations about treatment. Thus, examining the nature of patient beliefs and expectations regarding low back pain treatment will help coordinate efforts to improve consistency and quality of care. Methods This study was a cross-sectional population-based survey of adults living in Newfoundland, Canada. The survey included demographic information (e.g. age, gender, back pain status and care seeking behaviors) and assessed outcomes related to beliefs about the inevitable consequences of back pain with the validated back beliefs questionnaire as well as six additional questions relating beliefs about imaging, physical activity and medication. Surveys were mailed to 3000 households in July–August 2018 and responses collected until September 30th, 2018. Results Fout hundred twenty-eight surveys were returned (mean age 55 years (SD 14.6), 66% female, 90% had experienced an episode of LBP). The mean Back Beliefs Questionnaire score was 27.3 (SD 7.2), suggesting that people perceive back pain to have inevitable negative consequences. Large proportions of respondents held the following beliefs that are contrary to best available evidence: (i) having back pain means you will always have weakness in your back (49.3%), (ii) it will get progressively worse (48.0%), (iii) resting is good (41.4%) and (iv) x-rays or scans are necessary to get the best medical care for LBP (54.2%). Conclusions A high proportion of the public believe LBP to have inevitable negative consequences and hold incorrect beliefs about diagnosis and management options, which is similar to findings from other countries. This presents challenges for clinicians and suggests that considering how to influence beliefs about LBP in the broader community could have value. Given the high prevalence of LBP and that many will consult a range of healthcare professionals, future efforts could consider using broad reaching public health campaigns that target patients, policy makers and all relevant health providers with specific content to change commonly held unhelpful beliefs.
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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 | 0.008 |
| 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 | 0.000 |
| 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".