The resounding influence of benevolent childhood experiences
Bibliographic record
Abstract
Research with Indigenous communities has demonstrated the detrimental impacts of intergenerational trauma and disproportionate adverse childhood experiences (ACEs) on health and behavioral outcomes in adulthood. A more balanced narrative that includes positive childhood experiences is needed. The construct of benevolent childhood experiences (BCEs) facilitates assessment of positive early life experiences and their impact on well-being for Indigenous peoples. We consider associations between BCEs and well-being when taking into account ACEs and adult positive experiences. Participants are from Healing Pathways, a longitudinal, community-based panel study with Indigenous families in the Midwestern United States and Canada. Data for the current analyses are derived from 453 participants interviewed at wave 9 of the study. Participants reported high levels of positive childhood experiences in the form of BCEs, with 86.5% of the wave 9 participants reporting experiencing at least six of seven positive indicators. BCEs were positively associated with young adult well-being. This relationship persisted even when accounting for ACEs and adult positive experiences. While ACEs were negatively correlated with young adult well-being, they were not significantly associated with well-being when considering family satisfaction and receiving emotional support. Evidence of high levels of BCEs reflects realities of strong Indigenous families and an abundance of positive childhood experiences.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".