An Examination of the Impact of Psychosocial Factors on Mother-to-Child Trauma Transmission in Post-Migration Contexts Using Interpretative Phenomenological Analysis
Bibliographic record
Abstract
Objectives: The impact of psychosocial factors and social support in the transmission of trauma related to migration and the mother–child dyad has not yet been amply explored. This article examines this impact and the role that psychosocial factors may have in the transmission of the traumatic experiences of migrant mothers to their children. Patients and method: This study was conducted in France and focused on 14 mother–child dyads in which mothers were exposed to potentially traumatic events in the absence of the child, before or after birth. To analyse the corpus of information collected, the team used a qualitative method based on Interpretative Phenomenological Analysis (IPA) guidelines. Results: The study’s findings show that a lack of support from the family and lack of support from the host country are two of the major psychosocial factors involved in the exacerbation of maternal challenges. This negative impact on the maternal function leads to mirror reactions between the mother and child marked by the transmission of depressed moods and instinctive behavioural disorders, such as insomnia. Among other findings, factors are identified that help protect mother–child interactions, including religion and faith in God. Conclusion: These findings provide a foundation for further studies into the transmission of trauma from mother to child among migrant women and will help direct further clinical insight into the role of psychosocial factors in traumatic experiences and their transmission.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".