Korean sibling caregivers of individuals diagnosed with schizophrenia
Bibliographic record
Abstract
Siblings of individuals diagnosed with schizophrenia are an important source of family caregiving. Unfortunately, limited information is available about sibling caregivers because existing studies have focused on other family relationships such as parents, spouses, and children. To fill the knowledge gap, the purpose of this study is to describe Korean sibling caregivers' experience with individuals diagnosed with schizophrenia. Guided by Colaizzi's descriptive phenomenological methodology, we conducted in-depth, semi-structured, face-to-face interviews with eight individuals who have a sibling (1) diagnosed with schizophrenia and (2) hospitalized in an inpatient psychiatric unit. We discerned six key themes: sorrow, burnout, shame, different perspectives in life, acceptance, and responsibility. We categorized these themes into three groups: suffering, hope, and responsibility and obligation. Sibling caregivers of individuals with schizophrenia experience a mixture of several emotions. Participants loved their brother or sister with schizophrenia, but at the same time they felt shame and fear. While they were burdened by the responsibilities of caregiving, they remained loyal to their sibling with schizophrenia, continuing to help their siblings reach their full potential. Although participants were confused about the symptoms of schizophrenia, they were committed to learning more about the illness. Because we conducted the current study in Korea, the findings of this study may be unique to Korea culture. Further studies are needed to compare and contrast nuanced differences in sibling caregivers' experience among different cultural groups.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".