Beyond Empowerment in Rheumatology Care
Bibliographic record
Abstract
In this issue of The Journal of Rheumatology, Carluzzo et al1 explored different factors that contribute to the empowerment of individuals with arthritis. The study used data obtained from 12,560 US participants in the Live Yes! INSIGHTS program, based on sociodemographic information and patient-reported outcome measures (PROMs) about physical and mental health, emotional support, and empowerment. The instruments used in the study included the Patient Reported Outcomes Measurement Information System (PROMIS)-29 Profile v2.1, the PROMIS Emotional Support Short Form v2.0, and the Healthcare Empowerment Questionnaire (HCEQ) to measure empowerment. The main questions were these: “(1) What is the relationship between key study variables (sociodemographics, arthritis type, physical and mental health, and emotional support) and patient empowerment; and (2) Which characteristics contribute most to explaining differences in patient empowerment outcomes?”1 Emotional support, physical health, gender, arthritis type, and education were the most relevant factors associated with empowerment. Further, the authors highlight the importance of emotional support to positively affect the experience of empowerment and, thus, care and outcomes. The authors refer to 2 measures of empowerment: Patient Information Seeking (patients’ ability to ask questions and get explanations and advice) and Healthcare Interaction Results (patients’ experiences with talking to providers, obtaining answers, having their choices respected, and getting help and information). A third measure, Degree of Control, was excluded during the validation of the HCEQ due to patient feedback and to account for contextual factors in the US health insurance system.2 All 3 of these measures reflect an approach to empowerment as a construct that exists at an individual level for the patient and within specific patient-provider interactions. While addressing empowerment at this level is necessary to optimize direct care for individuals with arthritis—and rheumatic patients in general— it does not account for the full picture of what empowerment … Address correspondence to Dr. I. Peláez-Ballestas, Dr. Balmis 148. Col. Doctores.Cuahtémoc 06720, Mexico City, Mexico. Email: pelaezin{at}gmail.com.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".