POS1475 EMPLOYMENT TRAJECTORY OF CANADIAN YOUNG ADULTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".