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Record W4396615297 · doi:10.1111/ijsa.12475

Examining the efficacy of inoculation and value‐affirmation interventions in improving precandidate reactions among prospective military recruits

2024· article· en· W4396615297 on OpenAlexafffundabout
Justin R. Feeney, Ben Sylvester, Steve Gooch

Bibliographic record

VenueInternational Journal of Selection and Assessment · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsGovernment of CanadaDepartment of National Defence
FundersSocial Sciences and Humanities Research Council of CanadaDefence Research and Development Canada
KeywordsPsychologyPsychological interventionValue (mathematics)Military personnelSocial psychologyClinical psychologyApplied psychologyPsychiatryPolitical scienceStatistics

Abstract

fetched live from OpenAlex

Abstract This study engaged 4848 first‐time, English‐speaking prospective Canadian Armed Forces applicants to evaluate pre‐application interventions' efficacy on the Practice Canadian Forces Aptitude Test (PCFAT). Using a five‐level between‐subjects design, participants were randomly assigned to one of the following intervention conditions: inoculation message, value‐affirmation message, a combination of both, placebo writing intervention, or a no‐intervention control group. The interventions were anchored in inoculation theory and value‐affirmation theory and aimed to reduce math anxiety and close the gender gap in test performance. Contrary to expectations, the interventions did not significantly reduce math anxiety or improve problem‐solving performance. Consistent with the literature, a negative relationship was found between levels of math anxiety and problem‐solving scores, and men outscored women in problem‐solving across all conditions. Despite these outcomes, the study lays a foundation for future research on enhancing pre‐applicant experiences in an increasingly competitive labor market. Implications and future directions are discussed.

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 score0.080
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.316
Teacher spread0.289 · 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
Published2024
Admission routes3
Has abstractyes

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