Distressed and distracted by COVID-19 during high-stakes virtual interviews: The role of job interview anxiety on performance and reactions.
Bibliographic record
Abstract
Employers have increasingly turned to virtual interviews to facilitate online, socially distanced selection processes in the face of the COVID-19 pandemic. However, there is little understanding about the experience of job candidates in these virtual interview contexts. We draw from Event System Theory (Morgeson et al., 2015) to advance and test a conceptual model that focuses on a high-stress, high-stakes setting and integrates literatures on workplace stress with literatures on applicant reactions. We predict that when applicants ruminate about COVID-19 during an interview and have higher levels of COVID-19 exhaustion, they will have higher levels of anxiety during virtual interviews, which in turn relates to reduced interview performance, lower perceptions of fairness, and reduced intentions to recommend the organization. Further, we predict that three factors capturing COVID-19 as an enduring and impactful event (COVID-19 duration, COVID-19 cases, COVID-19 deaths) will be positively related to COVID-19 exhaustion. We tested our propositions with 8,343 job applicants across 373 companies and 93 countries/regions. Consistent with predictions, we found a positive relationship between COVID-19 rumination and interview anxiety, and this relationship was stronger for applicants who experienced higher (vs. lower) levels of COVID-19 exhaustion. In turn, interview anxiety was negatively related to interview performance, fairness perceptions, and recommendation intentions. Moreover, using a relevant subset of the data (n = 6,136), we found that COVID-19 duration and deaths were positively related to COVID-19 exhaustion. This research offers several insights for understanding the virtual interview experience embedded in the pandemic and advances the literature on applicant reactions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".