Attending work with chronic pain is associated with higher levels of psychosocial stress
Bibliographic record
Abstract
Background and Aims: Much is known about the impact of pain in terms of medical costs and missed work. Less is known about its associations when individuals are present for work. This study examines “presenteeism” by analyzing the psychosocial costs of pain in the workplace, using the 2015 European Working Conditions Survey (EWCS).Methods: We conducted cross-sectional analysis of 2384 individuals with chronic pain and 2263 individuals without pain (matched by age and sex) using data from the 2015 EWCS. We compared groups in terms of the following psychosocial factors: supervisor support, job responsibility, team cohesion, discrimination, threats/abuse, job competency, job reward, sexual harassment, stress, and job security. The groups were also compared in terms of days lost due to illness.Results: People with pain were 64% less likely to view their job as rewarding (odds ratio [OR] = 0.61; 95% confidence interval [CI], 0.57–0.65), 47% more likely to be subjected to threats/abuse in the workplace (OR = 0.68; 95% CI, 0.63–0.73), 30% more likely to report poor supervisor support (OR = 0.77; 95% CI, 0.73–0.82), and 28% more likely to perceive discrimination in the workplace (OR = 0.78; 95% CI, 0.71–0.85). People with pain missed approximately nine more days of work per year than respondents without pain.Conclusions: Chronic pain was associated with lower vocational fulfillment and feelings of being ostracized in the workplace. These findings suggest that the presence of pain in the workplace goes well beyond lost productivity due to absenteeism.
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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 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".