How Are Determinants of Health Inequities Related to Decision-Making in Juvenile Idiopathic Arthritis Care? A Narrative Synthesis
Bibliographic record
Abstract
Objectives To make high-quality decisions about juvenile idiopathic arthritis (JIA) treatments, youths and their caregivers should receive information about treatment options, explore which benefits and harms matter most to them, and consider their preferences and values. Shared decision making (SDM) allows youths, their caregivers and health providers (HCPs) to make high-quality decisions. Our team previously explored decisional needs in JIA, but no studies have summarized how determinants of health inequities are related to decision-making in JIA. We aimed to summarize how determinants of health inequities are related to decision-making in studies exploring decisional needs in JIA. Methods We systematically searched MEDLINE, Embase and PsycInfo from database inception to September 2024 for studies assessing decisional needs in JIA from the perspectives of youths with JIA, their caregivers and HCPs. Two team members independently screened citations and extracted data by examining excerpts narratively. We extracted data related to decisional needs and determinants of health inequities inspired by the Campbell and Cochrane Equity Methods Group’s PROGRESS-Plus framework (sex, gender, cultural factors, sociodemographic factors, education level, place of residence, occupation, religion, and social capital). Results We found 4297 records and included 80 studies. While most studies recorded determinants of inequities among participants (n=73), few explored the links between these determinants and decision-making in JIA (n=16), with 5 studies examining links with sex and gender, 5 with cultural factors, 2 with sociodemographic factors, 3 with education level and 2 with place of residence. Three studies looked at the use and interest of complementary health approaches among young people with JIA and found no links with sex, gender, race, or maternal education. A study found different treatment preferences according to gender. Three studies showed the influence of cultural factors on treatment decisions in JIA: HCPs took the patient’s sociocultural context into account when choosing which treatment options to present to families, HCPs thought that families’ cultural beliefs sometimes made treatment decision-making more difficult and influenced youths’ use of medication. Two studies with pediatric rheumatologists in Canada and the Netherlands found non-statistically significant differences in the factors they prioritized when deciding to discuss the withdrawal of biologics. Conclusion Few studies looked at the links between determinants of inequities and decision-making in JIA. More research would further the understanding of decisional needs in JIA based on these determinants of inequities and may help youths with JIA, their parents/caregivers and HCPs to make decisions adapted to their characteristics and contexts.
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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 | 0.020 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".