Unmet Workplace Support Needs and Lost Productivity of Workers With Systemic Sclerosis: A Path Analysis Study
Bibliographic record
Abstract
OBJECTIVE: Few studies have examined how workplace support can address work productivity loss among individuals with systemic sclerosis (SSc). The objective was to 1) examine the relationship between unmet workplace support needs and work productivity loss among workers with SSc, and 2) determine whether SSc symptom severity, fatigue, active disease periods, and workplace activity limitations mediate the relationship between unmet workplace support needs and work productivity loss. METHODS: A cross-sectional survey was conducted of employed individuals with SSc who were recruited through rheumatology clinics. Information on work productivity loss (i.e., absenteeism, presenteeism, job disruptions) and the need, availability, and use of workplace supports was collected. SSc symptom severity (e.g., workplace activity limitations, active disease periods, fatigue, and overall SSc symptom severity) and demographic, health, and work context characteristics were collected. Three Bayesian path models examined the association between unmet workplace support needs and each work productivity loss outcome. SSc symptom severity variables were examined as mediators in each model. RESULTS: A total of 110 employed participants were recruited (mean ± SD ages 49 ± 12.9 years). More than three-fourths of participants were female (77%) and worked full-time (77%). The most needed workplace supports included extended health benefits (84%), special equipment (63%), and flextime (59%). Additionally, 61% reported unmet workplace support needs. Path models indicated that indirect relationships between unmet workplace support needs and work productivity loss were significant. For all models, workplace activity limitations mediated the relationship between unmet workplace support needs and productivity loss. CONCLUSION: To foster productive employment of individuals with SSc, interventions need to address symptom severity and meet workplace support needs.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".