Prompting Metacognition During a Job Search: Evidence from a Randomized Controlled Trial with University Job Seekers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".