Lessons learned from conducting a study of emotions and positive personality change in Syrian origin young adults who have recently resettled in the Netherlands
Bibliographic record
Abstract
Post-traumatic growth is a compelling idea, yet extant research has often employed retrospective reports of change, rather than examining change over time. Research on samples of people that are traditionally seen as hard-to-reach are rare within personality psychology. In Karakter , we assessed a sample of Syrian origin young adults who recently resettled in the Netherlands (initial N = 168) four times over a 13-month period to examine experiences of adversity, emotions, and positive personality change. Here, we provide a detailed narrative of the research process, beginning with a description of how we incorporated open science practices in Karakter . We then turn to a discussion of the changes, challenges, and opportunities we encountered in the research. In doing so, we discuss conceptual and methodological considerations when examining personality change. We close with suggestions for researchers who are interested in conducting similar studies with populations that are underrecruited in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".