Prevalence of lower back pain and physical inactivity: the impact of psychosocial factors in pregnant women served by the Family Health Strategy
Bibliographic record
Abstract
OBJECTIVE: This study analyzed the impact of psychosocial factors on pregnant women with lower back pain and an associated lack of physical activity prior to pregnancy. METHODS: The sample included 66 pregnant women who were randomly selected from a total of 84 patients in the waiting rooms of the Family Health Units in Cuitegí, Paraíba, from September to November 2009. An epidemiological questionnaire adapted from the Quebec Back Pain Disability Scale was used for data collection. The questions about back pain, physical activity, and psychosocial factors were emphasized. SPSS 16.0 was used for the data analysis. The prevalence of lower back pain and its relationship to gestational age, habitual physical activity, and psychosocial factors were studied using the descriptive statistics and relative percentages in the SPSS Crosstabs procedure. The odds ratio and 95% confidence interval for lower back pain were calculated. RESULTS: The prevalence of lower back pain was 75%, which suggests that psychosocial factors were related to the presence of pain. Anxiety was reported in 42.8% of the women with lower back pain, and 38.7% of the women with lower back pain experienced physical fatigue at the end of the day. A higher percentage of pain (53%) was noted in the women who did not exercise prior to pregnancy. CONCLUSION: Lower back pain prior to pregnancy is associated with lack of physical activity and with psychosocial factors in the Family Health Strategy patients of Cuitegí county.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".