Bibliographic record
Abstract
OBJECTIVES: This descriptive study was conducted to determine pain and self-efficacy levels of individuals with osteoarthritis. METHODS: 83 patients, who were hospitalized in and admitted to physical therapy or rehabilitation outpatient clinic and had a primary diagnosis of osteoarthritis, were included in the study. The data of the study were collected by using patient information form, visual analog scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and arthritis self-efficacy scale. Pearson correlation analysis, t-test, and one-way analysis of variance were used to assess the data. A p value less than 0.05 was considered as statistically significant. RESULTS: The most frequent complaints of 78.5% of the individuals with osteoarthritis were pain and limitation of movement. The pain experienced by 69.9% affected their daily life activities so much. VAS mean score of the participants was 5.7±2.3, their WOMAC mean score was 56.3±14.8, and their self-efficacy score was 103.7±29.5. Self-efficacy levels of those, who were men, had a higher educational level, were not housewives, were independent in daily life activities and did not have an additional chronic disease, were higher compared to the other groups (p<0.05). In this study, a negative correlation between self-efficacy scores and VAS and WOMAC scores and a positive correlation between VAS and WOMAC scores were found (p<0.05). CONCLUSION: It was determined that self-efficacy of the individuals with osteoarthritis was moderate and gender, educational level, status of independence, pain, and functional level affected self-efficacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".