Bibliographic record
Abstract
OBJECTIVES: A large percentage of employees experience persistent pain while at work. This situation can become costly to employers with large amounts of lost production-time, absenteeism, and long-term disability. The link or transition between working through (ignoring) pain and disabling pain is unknown. This paper presents the results of a controlled study examining the impact of persistent pain on performance in a working population. Benefits of early detection are discussed. METHODS: This was a controlled, repeated measures study using 3 types of measures: questionnaires (pain, pain anxiety, daily memory, and attention mistakes); actigraphic monitoring to assure the absence of sleep deprivation; and the Performance Assessment Battery, a computer-based series of tests. Participants were studied during 3 time periods (9:00 AM, 3:00 PM, and 9:00 PM). RESULTS: Forty participants (20 pain, 20 controls) were studied. For all tasks, pain participants were slower than controls with significant findings on 2 tasks and less accurate with significant differences on 1 task. DISCUSSION: Unlike other studies that either induced pain or used persons with complex pain conditions, this study used participants with a low level of pain intensity and had a majority still engaged in full-time employment. Our results found that people with persistent low-level pain demonstrate a reduction in performance compared with controls. Our study revealed that using a sensitive tool to detect minor performance deficits could indicate pain interference. The early detection of pain interference would provide an opportunity for prevention programs to have a pre-emptive effect on work-related musculoskeletal disorders.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".