The Impact of Family Separation and Worry About Family on Psychological Adjustment in Refugees Resettled in Australia
Bibliographic record
Abstract
Few reliable predictors of postarrival psychological adjustment have been identified with regard to refugees once they arrive in their host country. We investigated the association between family separation and psychological symptoms in refugees resettled in Australia from 2013 to 2016. Participants were 1,495 adult refugees (M = 38.9 years, SD = 12.7) who participated in the Building a New Life in Australia population-based study across 4 years. Participants were assessed for psychological distress and posttraumatic stress symptoms (PTSS) using the Kessler Psychological Distress Scale (K6) and Posttraumatic Stress Disorder-8 (PTSD-8), respectively. We used latent class growth analysis (LCGA) to identify latent longitudinal trajectories and binary logistic regression to assess the contribution of family predictor variables toward PTSD-8 and K6 symptom trajectory class membership. The LCGA supported a four-class solution for PTSS, categorized as improving PTSS (18.4%), persistently high PTSS (11.5%), resilient PTSS (57.3%), and deteriorating PTSS (12.6%). For the K6, LCGA supported a four-class solution comprising classes categorized as persistently high psychological distress (PD; 7.0%), improving PD (17.3%), resilient PD (61.1%), and deteriorating PD (14.6%). Separation from family members did not independently predict the course of psychological symptoms; however, worry about family and friends contributed to the persistence of high PTSD-8 scores, OR = 1.75, and deteriorating K6 scores, OR = 1.57. The current findings suggest persistently high or worsening psychological symptom trajectories during the postsettlement phase may be marked by worry about family and friends, in addition to older age and female gender, rather than separation alone.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".