Patient Empowerment Among Adults With Arthritis: The Case for Emotional Support
Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify differences in patient empowerment based on biopsychosocial patient-reported measures, the magnitude of those differences, and which measures best explain differences in patient empowerment. METHODS: This was a cross-sectional observational study of 6918 adults with arthritis in the US. Data were collected from March 2019 to March 2020 through the Arthritis Foundation Live Yes! INSIGHTS program. Patient empowerment, measured by the Health Care Empowerment Questionnaire, included 2 scales: Patient Information Seeking and Healthcare Interaction Results. Patient-reported outcomes were measured using the Patient Reported Outcomes Measurement Information System (PROMIS)-29 and PROMIS emotional support scale. ANOVA assessed differences between groups, and Spearman rank correlation assessed correlations between variables. Hierarchical regression analysis determined the contributions of sociodemographic characteristics, arthritis type, and patient-reported health measures in explaining patient empowerment (α = 0.05). RESULTS: Empowerment was lower among those who were male, older, less educated, or who had lower income, osteoarthritis, less emotional support, or better physical function, although the effect was small-to-negligible for most of these variables in the final regression models. Empowerment did not differ by race/ethnicity in unadjusted or adjusted analysis. In final regression models, emotional support contributed the most to explaining patient empowerment. CONCLUSION: Emotional support is important for patient empowerment. This suggests that programs that seek to improve patient empowerment should target and measure effects on emotional support.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".