What to do and what works? Exploring how work groups cope with understaffing.
Bibliographic record
Abstract
Complaints regarding understaffing are common in the workplace, and research has begun to document some of the potential ill effects that can result from understaffing conditions. Despite evidence that understaffing is a relatively prevalent and consequential stressor, research has yet to explore how work groups cope with this stressor and the efficacy of their coping strategies in mitigating poor group performance and burnout. The present study examines these questions by exploring both potential mediating and moderating coping effects using a sample of 96 work groups from four technology organizations. Results indicate that work groups react differently to manpower and expertise understaffing conditions, with leaders engaging in more initiating structure behaviors when faced with manpower understaffing and engaging in more consideration behaviors when faced with expertise understaffing. Further, leaders' use of consideration in the face of expertise understaffing was negatively associated with group burnout. We also uncovered evidence that leadership behaviors and work group actions (i.e., team-member exchange) moderate relationships between manpower understaffing and outcomes, though differently for group performance and burnout. Overall, this study helps to reframe work groups as active in their efforts to cope with understaffing and highlights that some coping strategies are more effective than others. Implications for theory and practice are discussed. (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 | 0.005 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".