The impact of stress on the BarOn EQ-i® reported scores and a proposed model of inquiry. High Performing Systems
Bibliographic record
Abstract
This study looked at the impact of a “normal ” mindset versus a “stressed ” mindset on the reported scores of the BarOn EQ-i instrument, a self-report instrument that purports to measure emotional intelligence. The results indicated that with a simple set of instructions asking respondents to assume a very stressed mindset, significant downward changes in the total emotional intelligence and all 15 subscale scores were observed. The significant main effect for mindset has numerous implications, the most obvious being that individuals should not complete the instrument while in a stressed mindset. A second implication is that the relationship between emotional intelligence and stress might be such that stress actually reduces an individual’s ability to use his/her full emotional intelligence capacity. The dynamic relationship among emotional intelligence, stress and leader performance might also be visualized and predicted through the use of a catastrophe theory model. Stress is one of the major factors leaders must contend with in today's workplace. Tangri (2003) states: Stress costs American business more than $300 Billion annually in lost productivity, absenteeism, accidents, employee turnover, and medical, legal and insurance fees, and workers ’ compensation awards. This is more than 15 times the cost of all strikes combined. In Canada, the annual cost to business is $16 Billion, which is equivalent to 14 % of total net profits. Total costs to employers for accidents and work-related ill health in the United Kingdom is £7.3 Billion.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".