How Are Determinants of Health Inequities Related to Decision-Making in Juvenile Idiopathic Arthritis Care? A Narrative Synthesis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,088 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,011 | 0,012 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».