A Narrative Inquiry Into Global Systems Change to Support Families When a Parent Has a Mental Illness
Bibliographic record
Abstract
The issues that confront families when a parent experiences mental illness are complex. This often means that multiple service systems must be engaged to meet families' needs, including those related to intergenerational experiences of mental health and illness. A multisystem approach to public mental health care is widely recommended as a form of preventative intervention to address the effects of mental illness and its social, psychological, and economic impact upon parents, children, and families. Globally, a multisystemic approach to care requires a change in the way systems are currently organized to support families, as well as the way systems are interacting with families, and with each other. This qualitative secondary analysis emerged from a primary study examining global systems change efforts to support families, including components of change that were common and considered successful in different countries. A narrative inquiry method was used to re-analyze the data by compiling the stories of change described by individuals from participant countries. The data were interrogated to ask questions about story content, and to identify who was telling the story and how they described important changes across different geographical and cultural contexts. The individual stories of 89 systems change experts from 16 countries were then compiled into a shared global narrative to demonstrate international progress that has occurred over time, toward multisystemic change to support families where parents experience mental illness. While the global narrative demonstrates considerable overlap between pathways toward change, it is also important to document individual stories, as change pertains differently in different contexts. The individual stories and the global narrative illustrate how countries begin a journey toward change at different time points and may have various outcomes in mind when they commence. Study findings raise questions about the extent to which systems change can be standardized across countries that have unique social, cultural, political, and economic features. This study provides several potential points of reference for countries considering, or currently undertaking systems change to support families where a parent has a mental illness. It also provides an important story about international efforts undertaken to improve outcomes for 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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".