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Record W4414353379 · doi:10.1080/00223891.2025.2555358

Fear of Failure as Motivation: A Novel Conceptualization and Measure

2025· article· en· W4414353379 on OpenAlexafffund
Laurence Boileau, Patrick Gaudreau, Philippe Pétrin‐Pomerleau

Bibliographic record

VenueJournal of Personality Assessment · 2025
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsNomological networkConceptualizationConfirmatory factor analysisConstruct (python library)Fear of failureScale (ratio)PsychometricsExploratory factor analysis

Abstract

fetched live from OpenAlex

The Fear of Failure as Motivation Scale (FOFAMS) addresses the motivational role of fear of failure, which existing measures typically overlook in favor of its negative effects. FOFAMS was developed using a rational-theoretical approach to fill this gap, offering a new tool for examining this aspect in achievement contexts. Two studies examined the scale’s psychometric properties. Study 1 involved item development and an exploratory factor analysis, refining the scale by removing conceptually and psychometrically weaker items. Study 2, with samples of students (N = 385) and sports participants (N = 382), supported the unidimensional structure through confirmatory factor analysis and contributed evidence supporting the scale’s construct validity. FOFAMS showed significant correlations with other constructs hypothesized to relate to fear of failure as motivation. This questionnaire provides a unique perspective, enhancing the understanding of fear of failure’s positive influences on goal pursuit and achievement, and contributing to the broader nomological network of this construct.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.424
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.434
Teacher spread0.391 · 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 teacher head, 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

Citations1
Published2025
Admission routes2
Has abstractyes

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