Factors Within Families and Their Ecological Contexts That Shape Their Health and Well-Being: A Legacy Chapter
Bibliographic record
Abstract
This chapter provides a narrative of the early training experiences that shaped my career as well as an accounting of how my thinking about research and clinical practice has evolved over the years. I share examples from my research focused on family and ecological influences on mental health, concentrating in particular on research with families who have immigrant and refugee backgrounds and the interventions we have developed based on this research. I attempt to illustrate how my research has grown to become increasingly community-engaged, increasingly focused on building strengths in addition to understanding challenges, and increasingly framed within a social justice lens. I also discuss my clinical perspective on working with children and adolescents, which is grounded in family systems and developmental psychopathology models. My perspectives on assessment and intervention include the importance of self-reflection, striving for cultural safety and humility, identifying the implicit assumptions we make about mental health, and bringing an understanding of structural inequities into case conceptualizations and treatment plans. These developments in my thinking mirror changes in the field of clinical psychology more broadly. Finally, I share some future directions and lessons learned over the years that transcend specific research or clinical activities.
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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 | 0.000 | 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".