Unaccompanied Asylum-Seeking Refugee Children’s Forced Repatriation: Social Workers' and Police Officers’ Health and Job Characteristics
Bibliographic record
Abstract
During the past ten years the number of unaccompanied asylum-seeking refugee children has dramatically increased in Sweden. Some of them are permitted to stay in the receiving country, but some are forced back to their country of origin. Social workers and police officers are involved in these forced repatriations, and such complex situations may cause stressful working conditions. This study aimed to bridge the gap in knowledge of the relationship between general mental health and working with unaccompanied asylum-seeking refugee children who are due for forced repatriation. In addition, the role of psychosocial job characteristics in such relationships was investigated. A questionnaire including sociodemographic characteristics, the Swedish Demand-Control-Support Questionnaire, and the 12-item General Mental Health Questionnaire were distributed nationally. Univariate and multivariable regression models were used. Poorer mental health was associated with working with unaccompanied asylum-seeking refugee children among social workers but not among police officers. Psychological job demand was a significant predictor for general mental health among social workers, while psychological job demand, decision latitude, and marital status were predictors among police officers. Findings are discussed with special regard to the context of social work and police professions in Sweden.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".