Candidates' integration of individual psychological assessment feedback
Bibliographic record
Abstract
Purpose – The purpose of this paper is to empirically verify a theoretical model of candidates' feedback integration in the context of individual psychological assessment (IPA). Design/methodology/approach – Structural equation modeling analyses were conducted in a two-wave longitudinal study. A total of 97 candidates completed questionnaires immediately after their feedback session as well as three months later. Findings – Results indicate that candidates' motivational intention to act on IPA feedback is a pivotal variable linking feedback perceptions and post-feedback behaviors. Source credibility, assessment face validity, as well as perception that the feedback helped increase candidate's awareness were related to motivational intention. Conversely, feedback acceptance was not related to candidates' motivation to act on feedback and post-feedback behaviors. Research limitations/implications – Because the authors relied on self-report questionnaires, future studies would benefit from including externally assessed behavioral outcomes. Future research efforts should continue distinguishing candidates' acceptance and awareness based on their distinctive contributions in the feedback integration process. Practical implications – The results indicate that motivation created during the feedback session is a stronger predictor of day-to-day behavioral changes than it is of involvement in specific developmental activities. Originality/value – This research fills a gap in IPA literature by highlighting some IPA benefits and the processes involved in increasing feedback value for the participant.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".