Exploring Outcomes through Narrative: The Long‐term Impacts of Better Beginnings, Better Futures on the Turning Point Stories of Youth at Ages 18–19
Bibliographic record
Abstract
This study examined the long-term effects of the Better Beginnings, Better Futures project, a community-based early childhood development program, on 18-19 year-old youths' narratives about turning points in their lives. The sample consisted of youth who participated in Better Beginnings from ages 4-8 (n = 62) and youth from a comparison community who did not participate in Better Beginnings (n = 34). Controlling for covariates, significant differences favoring youth from the Better Beginnings sites were found on several dimensions of the turning point stories: ending resolution, personal growth, meaning-making, coherence, and affect transformation. Effect sizes ranged from .45 to .76 for these outcome dimensions, indicating moderate to large effects. Also, turning point story dimensions were found to be significantly correlated with two standardized measures of well-being: youths' self-esteem and community involvement. Youths' self-esteem was directly related to story ending resolution, personal growth, and meaning making, and youths' community involvement was directly related to story specificity, meaning making, and coherence. Family functioning was also examined in relation to these narrative dimensions but was not found to be significantly related to them. The findings suggest the utility of a narrative approach for the evaluation of the long-term outcomes of early childhood development programs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".