Pain coping strategies and related factors including demographics, pain characteristics, functional mobility in postmenopausal women with chronic musculoskeletal pain
Bibliographic record
Abstract
This cross-sectional study investigated pain coping strategies and their relationship to demographic and clinical characteristics in postmenopausal women (PMWs) with chronic musculoskeletal pain (CMSP). PmW (n = 60) who presented to receive physiotherapy from a rehabilitation center participated. McGill Pain Questionnaire (MPQ) was used to assess pain intensity and characteristics, Pain Coping Inventory (PCI) was used to assess strategies of coping with pain, and Timed Up and Go-Test (TUG) was used to assess functional mobility. Data were analyzed using descriptive analyses, paired-samples t-test, independent-samples t-test, Mann Whitney U-test, one-way ANOVA, and Pearson’s correlation analysis. There was no significant difference in terms of marital status, educational status, and exercise habits between the participants’ statuses of using active and passive strategies of coping with pain. Younger women (50–59 years of age) preferred active strategies more than passive strategies to cope with pain (p = .047). There were significant differences among the age groups in terms of “pain transformation” subdomain of active strategies (p = .007) and “sensory” subdomain of MPQ (p = .053). Strategies of coping with pain and functional mobility of participants were not significantly related (p > .05). Results indicated that age is a significant factor in coping with pain and pain characteristics. Healthcare providers should consider PmW’s preferences and experiences with pain management when recommending pain management strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".