Interaction effect of psychological distress and asthma control on productivity loss?
Bibliographic record
Abstract
Little is known about the potential synergistic effect of comorbid psychological distress (PD) and uncontrolled asthma (UA) on productivity loss. We estimated the productivity loss associated with the combination of these two potentially preventable conditions in employed adults with asthma. A population-based random sample of 300 adults with asthma in British Columbia, Canada, was prospectively recruited between Dec 2010 and Aug 2012. PD and productivity loss due to absenteeism and presenteeism was measured using validated instruments, and asthma control was ascertained using 2010 Global Initiative for Asthma management strategy. We used two-part regression models to study the contribution of UA and PD to productivity loss. Compared with reference group (controlled asthma (CA)+noPD), those with UA+noPD had CAD$286 (95%CI $276-297) weekly productivity loss, and those with CA+PD had CAD$465 ($445-485). Those with UA+PD had CAD$449 (437-462) in productivity loss. There was no significant interaction effect of PD with asthma control levels on productivity loss (p=0.22). In patients without PD, uncontrolled asthma was associated with a higher productivity loss than controlled asthma, but this was not the case in patients with PD. This finding can be explained by the fact that the contribution of PD to productivity loss is so large that there is no room for synergy with asthma control. Future studies should assess the impact of interventions that modify PD in patients with asthma.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".