Behavioral Integration of Individual Psychological Assessment Feedback: Assessor and Social Support
Bibliographic record
Abstract
The present study investigates the role of perceived social support and development-focused feedback techniques on behavioral integration of feedback in the context of individual psychological assessment. We hypothesized that development-focused techniques would predict participants' motivational intention to act on feedback and tested whether perceived social support would mediate or moderate the relationship between motivational intention and behavioral outcomes. We performed structural equation modeling analyses on data collected at two time-points. Two hundred and forty (N = 240) participants completed questionnaires immediately after their feedback session (T1) and 138 of them completed questionnaires three months later (T2). The model results, χ2 = 230.09, p < .01, CFI = .97, TLI = .97, SRMR = .06, RMSEA = .03 90% CI [.02, .05], suggest that development-focused techniques predict motivational intention, social support mediates the relationship between motivational intention and developmental activities (R2 = .31), and social support also interacts with development-focused techniques to predict behavior change (R2 = .40). The relationship between social support and behavioral change is higher when the assessor uses few development-focused techniques (at -1 SD, b = .32, p < .001, 95% CI [.27, .36]). The study provides empirical insights about how behavioral change unfolds in an IPA feedback context and suggests that participants could benefit from obtaining social support to act on feedback. Assessors should focus on development during feedback and encourage the participant to seek social support to facilitate their subsequent professional development. Because the findings rely on self-reported data, future studies would benefit from including observed measures.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".