Family Experience of Detention for Migratory Reasons: Findings From a Qualitative Systematic Review
Bibliographic record
Abstract
ABSTRACT In response to growing international migration, several states have adopted controversial measures, including detaining migrants, which have detrimental impacts on migrants' physical, mental and social health. Although family is often at the heart of migratory decisions, few studies have used a family lens to examine this phenomenon. This paper reports on the findings of a qualitative systematic review that aims to describe the experience of migrant families dealing with detention. Articles were searched in relevant databases, and the quality of studies was assessed independently by two reviewers using the JBI Critical Appraisal Checklists. Twenty‐two out of 897 articles met the inclusion criteria and were subsequently included. Three themes emerged from the thematic synthesis that capture the experience of migrant families affected by detention: (1) alterations of the family structure, (2) disruptions in instrumental and emotional functioning and (3) weakening of the family identity. Results robustly evidenced the detrimental effects of detention on families and the urge to stop this practice. Limitations included the difficulty of distinguishing between the experience of detention and deportation in some studies, as well as limited information about the long‐term effect of detention on family health and integration in the host society.
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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 | 0.062 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".