Rheumatic Disease Disclosure at the Early Career Phase and Its Impact on the Relationship Between Workplace Supports and Presenteeism
Bibliographic record
Abstract
OBJECTIVE: Young adults with rheumatic disease face challenges communicating health needs, accessing workplace support, and sustaining productivity. Our objective was to examine whether disclosure modifies the relationship between workplace support and presenteeism. METHODS: An online survey was administered to Canadian young adults with rheumatic disease and asked about presenteeism (0 = health had no effect on work; 10 = health completely prevented working), workplace support need, availability, and use and whether health details were disclosed to an immediate supervisor. A multivariable robust linear regression model was conducted and stratified by those who did and did not disclose the details of their health to their supervisor. RESULTS: A total of 306 participants completed the survey with a mean ± SD presenteeism score of 4.89 ± 2.65. More than 70% disclosed health details to their supervisor; those who disclosed reported greater presenteeism (mean ± SD 5.2 ± 2.5) when compared to those who did not disclose (mean ± SD 4.2 ± 2.61). Greater disease severity was associated with disclosure. Half of the participants reported unmet workplace support needs (53%), 32% reported that their workplace support needs were met, and 15% reported exceeded workplace support needs. The relationship between presenteeism and workplace support needs was modified by disclosure. For participants who disclosed, workplace support needs that were unmet (β = 1.59 [95% confidence interval (95% CI) 0.75, 2.43]) and that were met (β = 1.25 [95% CI 0.39, 2.11]) were associated with greater presenteeism when compared to those with exceeded workplace support needs. CONCLUSION: To address presenteeism, strategies should be developed for young adults with rheumatic disease to foster access to available workplace supports and to navigate disclosure decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".