How children understand parental mental illness: "you don't get life insurance. What's life insurance?".
Bibliographic record
Abstract
OBJECTIVES: To understand how children living with parental mental illness (PMI) understand mental illness (MI) and what they want to tell other children. METHOD: The study design was a secondary analysis of a grounded theory study exploring Canadian children's perceptions of living with PMI. Interviews from 22 children, ages 6 - 16, living with a parent with depression, bipolar disorder or schizophrenia receiving treatment for the MI, were re-read, coded and analyzed along with data categories, their properties, field notes and memos from the original data. RESULTS: Children revealed that they had limited understanding of MI and received few factual explanations of what was happening. Limited information on MI caused undue hardship. Younger children worried about their parent dying, while older children also were concerned about developing MI. Children offered suggestions for other children in similar circumstances. CONCLUSIONS: This study raises awareness of children living with PMI and identifies them as a population requiring services. It incorporates children's perceptions of what they know and need to know. Children require assistance to understand and to respond to PMI. Mental health and primary health care clinicians have opportunities to assist these children within collaborative care models developed in conjunction with school services.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".