Experiences of medical traumatic stress in parents of children with medical complexity
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Parents of children with medical complexity (CMC) experience high levels of stress and adverse mental health outcomes. Pediatric medical traumatic stress (PMTS) could be an important contributor that has not yet been explored. PMTS describes parents' reactions to their child's illness and medical treatment and can lead to post-traumatic stress symptoms. This is the first study to describe the experiences and impact of PMTS among parents of CMC. METHODS: We conducted semi-structured interviews with 22 parents of CMC. Reflexive thematic analysis was used to generate themes that described the experiences of PMTS and potential contributing factors in the healthcare setting. Themes were validated by study participants. RESULTS: Parents experienced a spectrum of events and circumstances that impacted PMTS. These corresponded to three major themes: (a) the distinctive context of being the parent of a CMC, (b) interactions with healthcare providers that can hurt or heal and (c) system factors that set the stage for trauma. The consequences of repeated PMTS were a common point of emphasis among all the themes. Parents identified numerous changes that could mitigate PMTS such as acknowledgement of trauma and provision of proactive mental health support. CONCLUSIONS: Our study highlights the issue of PMTS among parents of CMC and presents opportunities to mitigate their traumatic experiences. Supporting the integration of trauma-informed care practices, increasing awareness of PMTS and advocating for parental mental health services could better support parents and 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".