Rethinking work-health models for the new global economy: A qualitative analysis of emerging dimensions of work
Bibliographic record
Abstract
Technology change, rising international trade and investment, and increased competition are changing the organization, distribution and nature of work in industrialized countries. To enhance productivity, employers are striving to increase innovation while minimizing costs. This is leading to an intensification of work demands on core employees and the outsourcing or casualization of more marginal tasks, often to contingent workers. The two prevailing models of work and health - demand-control and effort-reward imbalance - may not capture the full range of experiences of workers in today's increasingly flexible and competitive economies. To explore this proposition, we conducted a secondary qualitative analysis of interviews with 120 American workers [6]. Our analysis identifies aspects of work affecting the quality of workers' experiences that are largely overlooked by popular work-health models: the nature of social interactions with customers and clients; workers' belief in, and perception of, the importance of the product of their work. We suggest that the quality of work experiences is partly determined by the objective characteristics of the work environment, but also by the fit of the work environment with the worker's needs, interests, desires and personality, something not adequately captured in current models.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".