Expressing pain and fatigue: A new method of analysis to explore differences in osteoarthritis experience
Bibliographic record
Abstract
OBJECTIVE: To apply a new method of analysis to 2 large qualitative datasets in order to examine differences in osteoarthritis (OA) experience according to the affected joint (knee or hip) and sex. METHODS: A secondary analysis of qualitative data from 2 studies was conducted. Study 1 comprised 28 focus groups with 50 men and 80 women (ages 47-92 years). Study 2 comprised 14 focus groups with 32 men and 56 women (ages 56-91 years). All participants had symptomatic OA. In Study 1, secondary analysis using comparative keyword analysis (CKA) compared relative frequencies of words uttered by participants experiencing knee pain with words used by participants experiencing hip pain. In Study 2, CKA compared words used by men with words used by women. Subsequent analysis explored the contexts in which participants used key words. RESULTS: All participants in Study 1 described concerns with their bodies, activity limitations, and pain management, but details of their concerns differed. People with knee pain focused on stairs, weight, and stiffness, while those with hip pain were concerned with sidedness and groin pain. In Study 2, both men and women discussed activity and interaction with spouses. However, men used more factual words, especially relating to enumeration, while women offered more explanation without prompting from others. CONCLUSION: CKA provides productive inroads into qualitative datasets. Understanding the different ways that affected joints are discussed, and sex differences in descriptions of OA, may lead to improvements in clinical assessment tools, better targeting of interventions, and enhanced communication between health care professionals and patients.
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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 | 0.074 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".