POS1475 EMPLOYMENT TRAJECTORY OF CANADIAN YOUNG ADULTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Notice bibliographique
Résumé
Background Young adulthood, 18-30 years, is a time when many individuals start working. Failing to establish employment during young adulthood could predict lifetime financial hardships. Lack or limited employment could limit access to healthcare benefits, adversely affecting treatment access and health outcomes. Objectives To determine the average employment trajectory of childhood- and adult-onset systemic lupus erythematosus patients in young adulthood (YASLE). Methods Patients (with ≥ 2 visits) were from two longitudinal cohorts: the Canadian national lupus cohort (via the Canadian Network for Improved Outcomes in SLE, CaNIOS) and the University of Toronto (UT) lupus cohort from Toronto Western Hospital. The CaNIOS cohort (2002-2020) included participants from multiple provinces, the UT cohort only Ontario patients (1983-2020). Participants report employment states annually. The employment states were: employed, unemployed, homemaker, student, work disabled. This was reduced to: employed, unemployed or not in labour force (NLF, student, homemaker, work disabled) for modelling. The Markov multistate model (msm) was used to model employment trajectory. Transition probabilities at 1, 6, 12 years from age 18 years were calculated. Results 841 participants (85.4% females): 253 (CaNIOS) and 588 (UT). Mean age (standard deviation, SD) at baseline (cohort entry) was 23.1 (SD 3.7) years. Participants’ age: 38.2% (18-20 years), 19.3% (21-23 years), 21.9% (24-26 years), 20.7% (27-30 years). 403 (47.9%) were cSLE. 89.5% completed high school. Median disease duration was 3.3 (0.7-6.6, 25th-75th percentile, P) years. Median follow-up was 2.8 (0.9- 6.5, 25th-75th P) years. At baseline, 16.6% were employed, 5.6% were unemployed and 77.8% were NLF (42 work disabled, 226 homemakers, 386 students). 374/6615 (6%) visits showed state changes. 58% occurred in the NLF group. YASLE patients have the highest probabilities of remaining in the same employment state as baseline (Table 1). With increasing age, there was a reduced rate of staying employed (0.69 to 0.64). Those unemployed showed low probability to become employed (0.28 to 0.38). The NLF group has static rate of transition to employment (0.65), without expected increase with age. Conclusion YASLE patients showed minimal or no increase in transitions into employment from non-employed states, and no increase in employment with age as expected in the general young adult population. This could suggest a lowered labour force attachment in YASLE patients, suggesting difficulties in establishing employment during young adulthood. Future work should focus on YASLE patients’ perceived barriers and facilitators for employment, to target interventions for supporting patients’ employment. References [1]Yelin, E., et al. Arthritis & Rheumatism 2007; 57: 56-63. [2]E. F Lawson, et al. Arthritis Care Res 2014; 66: 717-24. Acknowledgements: NIL. Disclosure of Interests: Lily Lim Speakers bureau: Pfizer, Feb 2023. <$5000. Not related to this abstract., Menelaos Konstanidis: None declared, Zahi Touma: None declared, Diane Lacaille: None declared, Umut Oguzoglo: None declared, Christine Peschken Consultant of: For GSK, Astra Zeneca, unrelated to this abstract, <$5000, Nicole Anderson: None declared, Ramandeep Kaur: None declared, Eleanor Pullenayegum: None declared.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».