Prevalence of chronic pain and its characteristics among elderly people in Ahvaz city: a cross sectional study
Bibliographic record
Abstract
Introduction and purpose: One of the major health problems in old age is chronic pain. There are some evidences showing that elderlys chronic pain is not assessed and relieved adequately. Hence, it is essential to access accurate and sufficient information about chronic pain status to effectively manage the situation. Therefore, this study aimed to assess chronic pain prevalence and its characteristics among elderly. Materials and Methods: This is a cross sectional study conducted among 205 elderly patients with chronic pain using multistage cluster sampling method. Data were collected during a period of 6 months in Ahwaz health care centers. Short version of the McGill Pain Questionnaire was used for chronic pain measurement. The internal consistency was assessed using Cronbach alpha. Data were analyzed using SPSS software (version 21) via Independent sample T test. Findings: Findings from this study showed that the most prevalent chronic pain was knee pain (80%) while, the least one was abdominal pain (13%). Majority of our participants (85%) reported having pain in multiple locations and 15% suffering from pain in a single location. Regarding type of pain, we found that cramping pain (80%) was the most prevalent type and sickening pain (22%) was the least prevalent. In addition, the most intense pain was discomforting pain (33%) and the least one was intolerable pain (19%). Pain was significantly (P less-than 0.05) higher in women, older than 65 years old, singles and under diploma education. There was no significant (P greater-than 0.05) association between pain and home ownership as well as income. Conclusion: This study revealed that prevalence of chronic pain is high among old people. Attention to chronic pain features among elderly is essential for identifying vulnerable groups and delivering better treatments. The findings of this study can be used by researchers and policy makers to plan effective pain management interventions.
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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 | 0.001 |
| 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 | 0.000 |
| 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".