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Record W3016834277 · doi:10.1111/apps.12255

Prompting Metacognition During a Job Search: Evidence from a Randomized Controlled Trial with University Job Seekers

2020· article· en· W3016834277 on OpenAlexaff
Adam M. Kanar, Dave Bouckenooghe

Bibliographic record

VenueApplied Psychology · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsBrock University
Fundersnot available
KeywordsMetacognitionSeekersPsychological interventionPsychologyIntervention (counseling)Job attitudeJob performanceSocial psychologyApplied psychologyJob satisfactionCognitionPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Searching for a job is a self‐directed activity that requires self‐regulation over significant periods of time; yet, in the scientific community, we know little about proximal support interventions that shape the self‐regulation processes during a job search. Using an experimental design, we tested a proximal support intervention in the form of metacognitive prompts and how this shaped metacognition and effort in 123 university job seekers over a period of 9 weeks. Job seekers who were exposed to the intervention reported higher levels of metacognition, effort, and a greater number of job interviews compared to job seekers in a control condition. Additionally, the present study revealed that the prompting intervention, compared to the active control condition, resulted in higher levels of metacognition for job seekers who were less committed to finding a job than for job seekers who were more committed to finding a job. In summary, the current study makes important contributions to the job search literature and practice by fine‐tuning our understanding of the interventions that may support metacognition during the job search process and the individuals for whom these interventions may be most effective.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.039
GPT teacher head0.262
Teacher spread0.224 · 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.

Study designRandomized trial
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

Citations13
Published2020
Admission routes1
Has abstractyes

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