Adjusting to life after pediatric stroke: A qualitative study
Bibliographic record
Abstract
AIM: To examine adjustment after stroke in adolescence from the perspective of affected young people. METHOD: Fourteen participants (10 female) aged 13 to 25 years with a history of ischemic or hemorrhagic stroke in adolescence participated in one-on-one semi-structured interviews at the Hospital for Sick Children, Toronto, Canada. Interviews were audio-recorded and transcribed verbatim. Two independent coders conducted a reflexive thematic analysis. RESULTS: Five themes were identified as representative of adjustment after stroke: (1) 'Processing the story'; (2) 'Loss and challenges'; (3) 'I've changed'; (4) 'Keys to recovery'; and (5) 'Adjustment and acceptance'. INTERPRETATION: This qualitative study provides medical professionals with a personal, patient-driven lens through which to better understand the challenges of adjusting to life after pediatric stroke. Findings highlight the need to provide mental health support to patients to assist them in processing their stroke and adapting to long-lasting sequelae. WHAT THIS PAPER ADDS: Processing the onset event is a key component of adjustment to stroke. Feelings of anxiety, sadness, frustration, and self-consciousness impede adjustment to stroke. Young people may feel overwhelmed academically owing to neurocognitive deficits. Sequelae may rid young people of hobbies and passions, and alter plans for the future. To adjust to stroke, survivors draw on resilience, patience, determination, and social support.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".