Overloaded and stressed: A case study of women working in the health care sector.
Bibliographic record
Abstract
Although role overload has been shown to be prevalent and consequential, there has been little attempt to develop the associated theory. The fact that the consequences of role overload can be positive or negative implies that the relationship between role overload and perceived stress depends partly on the environment within which role overload is experienced (i.e., the perceived situation) and how the situation is evaluated (i.e., appraised). Guided by cognitive appraisal theory, this study applies qualitative methodology to identify the situation properties that contribute to variable stress reactions to role overload. In this in-depth examination, overloaded female hospital workers were asked to describe what makes role overload situations potentially stressful, to gain an insight into how role overload is appraised. A taxonomy listing 12 role overload situation properties was developed from the findings, providing the first known classification of the situation properties of role overload that can create the potential for stress. The results also reveal clues as to why some people suffer more stress during role overload than others, increase our understanding of the relationship between role overload and perceived stress, and provide a useful tool for examining the environment of role overload. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".