Much More Than PTSD: Mothers’ Narratives of the Impact of Trauma on Child Survivors and Their Families
Bibliographic record
Abstract
This study examined the narratives of ten Caucasian mothers whose children had been impacted by 'traumatic' events and referred to a specialist trauma service in N. Ireland. The research question was whether the PTSD construct adequately represented the broad 'lived' experience of the impact of trauma on survivors' wellbeing and their family relationships as articulated by mothers post trauma. Narrative Interviewing methodology was employed and the resulting data inductively organised into an evolving thematic framework. A quantitative analysis of the prevalence of particular themes is presented supplemented by qualitative quotations to illustrate the complexity of reported impact. The major components of the mothers' narratives included family and relational distress (35.7%), non-pathological individual distress (24.4%), resilience (16.7%) and a prior history of adversity (16.6%). Prior history of adversity was resent in 8 out the 10 cases including a high level of suicide. PTSD symptomatology constituted a small proportion of the narratives (6.6%) and this suggests that the PTSD construct does not adequately represent the broad 'lived' experience of the impact of trauma. Although a small and heterogeneous study sample, the findings are sufficiently robust to suggest further investigation is required to understand the phenomenological experience of trauma of child victims/survivors and their families.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".