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Record W4206433866 · doi:10.1111/ijsa.12365

The role of fear of negative evaluation in interview anxiety and social‐evaluative workplace anxiety

2021· article· en· W4206433866 on OpenAlexaff
Irene Zhang, Deborah M. Powell, Silvia Bonaccio

Bibliographic record

VenueInternational Journal of Selection and Assessment · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of OttawaUniversity of Guelph
Fundersnot available
KeywordsPsychologyAnxietyAntecedent (behavioral psychology)Fear of negative evaluationSocial anxietyJob interviewSocial psychologyClinical psychologyApplied psychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract We investigated the consequences of interview and social‐evaluative workplace anxiety on job performance, and whether these two anxieties share a common antecedent–fear of negative evaluation. Job applicants (n = 128) completed a survey following their interview and halfway through their work term; supervisory performance ratings were collected at the end of the work term. Fear of negative evaluation was positively correlated with both interview anxiety and social‐evaluative workplace anxiety. The correlation between interview anxiety and job performance was near zero, as was the correlation between social‐evaluative workplace anxiety and job performance; these relations were not moderated by the social‐evaluative nature of the job. This study suggests that anxious interviewees and employees perform as well as their less anxious counterparts, even in social‐evaluative jobs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.019
GPT teacher head0.323
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2021
Admission routes1
Has abstractyes

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