Understanding the Employment Landscape in People With Systemic Sclerosis
Bibliographic record
Abstract
Objective Systemic sclerosis (SSc) can restrict employment participation. Our objectives were to comparatively evaluate health factors, work factors, and workplace accommodations between those who are employed and those who recently gave up employment. Methods A cross-sectional study was conducted of employed and recently working, but now unemployed, individuals with SSc. Demographics, employment sectors, health factors, flare frequency, work context, and information about the need, availability, and use of workplace supports were collected. Results Participants were 140 individuals (108 [77.1%] women, 32 [22.9%] men), of whom 110 (78.6%) were employed and 30 (21.4%) were unemployed. Participants worked in education/health/sciences/arts (n = 51, 36.4%), sales/retail (n = 23, 16.5%), banking/insurance/business/technology (n = 22, 15.7%), government (n = 15, 10.7%), construction/utilities (n = 10, 7.1%), and manufacturing/agriculture/mining/ logging (n = 10, 7.1%). Employed participants had a lower mean age (48.4 vs 54.3 yrs), and higher level of education (77.3% with postsecondary education vs 22.7% without). Those who had no flares were more frequently employed (41.7%), compared to those who had 1 to 2 flares (35.2%) and ≥ 3 flares (23.1%). The availability of workplace accommodations differed significantly between the employed and unemployed: flexible hours (74.5% vs 40%,P= 0.0005), more rest periods (73.6% vs 46.7%,P= 0.0001), special equipment (82.7% vs 46.7%,P< 0.0001), and work schedule flexibility (66.4% vs 33.3%,P= 0.003). Conclusion Health factors alone do not differentiate those who are employed and those who gave up employment. This study lays the groundwork for where SSc-specific efforts in workplace policies and practices should be directed, especially workplace support.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".