Associations between measures of socio-economic status, beliefs about back pain, and exposure to a mass media campaign to improve back beliefs
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is one of the most common and costly healthcare problems worldwide. Disability from LBP is associated with maladaptive beliefs about the condition, and such beliefs can be influenced by public health interventions. While socioeconomic status (SES) has been identified as an important factor in health literacy and inequalities, not much is known about the association between SES and beliefs about LBP. Therefore, this study examined the relationship between measures of SES and the belief that one should stay active through LBP in a representative sample of the general population in Alberta, Canada. We also examined the association between measures of SES and self-reported exposure to a LBP mass media health education campaign. METHODS: Population-based surveys from 2010 through 2014 were conducted among 9572 randomly selected Alberta residents aged 18-65 years. Several methods for measuring SES, including first language, education, employment status, occupation, and annual household income, were included in multivariable logistic regression modeling to test associations between measures of SES and outcomes. RESULTS: Univariable analysis showed that age, language, education, employment, marital status, and annual household income were significantly associated with the belief that one should stay active through LBP. In multivariable analysis, income was the variable most strongly correlated with this belief (odds ratios ranged from 1.04 to 1.62 for the highest income category, p = 0.005). Univariable analysis for exposure to the campaign showed age, language, education, employment, and occupation to be significantly associated with self-reported exposure, while only education (p = 0.01) and age (p = 0.001) remained significant in multivariable analysis. CONCLUSIONS: Individuals with higher annual income appear more likely to believe that one should stay active during an episode of LBP. Additionally, targeted information campaigns are recalled more by low SES groups and may thus assist in reducing health disparities. More research is needed to fully understand the association between socioeconomic factors and LBP and to target campaigns accordingly.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".