A multi-method approach to assessing deadlines and workload variation among newspaper workers
Bibliographic record
Abstract
OBJECTIVES: Working under frequent deadlines was previously found to be associated with upper limb work-related musculoskeletal disorders (WMSDs) in newspaper workers. Further investigation was required so that concrete recommendations for change could be offered to the workplace parties (labour and management of a large metropolitan newspaper). STUDY DESIGN: The assessment was based on three methods. A questionnaire was used to clarify time-related aspects of work on deadlines for a larger group of workers. Experience sampling was used to document temporal variation in various aspects of physical and psychological demands over work shifts and deadline cycles. Focus groups were also conducted. RESULTS: Differences were found between the "High" and "Low" deadline groups: Those working with frequent deadlines more frequently were required: to work together with others, to perform tasks on a specific schedule and specific order, to work at a fast paced, to perceive their work as hectic and "hard". Experience sampling showed differential trends in workload across daily, weekly, and no deadline days. The lack of breaks for extended periods of time leading up to a deadline was noticeable. The focus groups were useful in highlighting issues not addressed by the other two methods and to understand the feasibility of various possible interventions. CONCLUSIONS: The integration of results from all methods lead to recommendations for issues upon which to focus prevention related activities where deadlines are present: delays in work flow from others, interruptions from technology related problems, excessive work, insufficient staff/insufficient time, extra/unexpected work, compromising of work quality for speed, and lack of time for breaks.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".