Introduction to Special Section: Advancing Research on the Intersection of Families, Culture, and Health Outcomes
Bibliographic record
Abstract
This Special Section of the Journal of Pediatric Psychology on Families, Culture, and Health Outcomes was prompted by increased calls for evaluation of culture in research and clinical work, a perceived dearth of research that accounted for culture in the context of family health beliefs and behaviors, and our own clinical translational programs incorporating culture into family intervention research. In that call, we summarized potential topics to “include the interface between families, culture, and illness management behavior, the identification of family-level risk and protective factors in disease outcomes, and the evaluation of culturally tailored family-based treatments for pediatric conditions.” Our hope is that the original, high-quality research and commentaries included in the special section compel others to investigate the cultural issues that impact their assessments, interventions, and clinical research in pediatric psychology. The imperative to address families, culture, and health outcomes is underscored by increasing racial, ethnic, and family diversity among the populations we serve, as well as the globalization of pediatric issues in which research and health outcomes for children are reciprocally informed. Data from the 2010 United States Census reveal that only 72.4% of respondents self-identified as “white alone,” indicating that more than a quarter of our current population identifies themselves as ethnic/racial minorities or mixed race (Hixson, Hepler, & Kim, 2011). The proportion of immigrants in the U.S. also continues to increase; in 2009, 11% of the population (33 million people) were second generation immigrants (U.S. born children of immigrant parents; MacArthur Foundation, 2012). Canada has even higher immigration rates than the U.S. In 2006, 19.8% of the Canadian population (6.5 million people) was foreign-born, or first-generation immigrants, resulting in great ethnocultural diversity (Statistics Canada, 2007). This trend is expected to continue in both in countries. By 2040, U.S. population projections indicate that 16.7% of the U.S. population will be foreign-born immigrants (MacArthur Foundation, 2012); in Canada, similar projections indicate 25% to 28% of the population will be foreign born by 2031 (Statistics Canada, 2007).
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.042 | 0.017 |
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".