Virtues, Resilience, and Well-Being of Indigenous Youth in Peru
Bibliographic record
Abstract
Objectives: The objective of this study was to observe the relation of Values In Action (VIA) virtues, well-being, and resilience within a unique, non-Western population of Indigenous youth in the Peruvian Amazon.Methods: Data were collected from students (n = 172, age range: 11-16 years) attending a rural village school via self-report surveys to assess relationships using the VIA Youth-96 (VIA-Y-96) Assessment, Personal Wellbeing Index (PWI-A), and the Child and Youth Resilience Measure (CYRM-28).Results: The factor analysis of the CYRM-28 yielded a 3-factor breakdown (Social Engagement, Cultural Citizenship, and Guidance) instead of eight. Different VIA virtues predicted each of the three factors of the revised 3-factor CYRM-21-Peru model (CYRM-21-P); Transcendence, Humanity, and Wisdom were predictors of well-being; and higher reported resilience leads to higher well-being. Most participants scored very high on the PWI-A.Implications: Research presented in this paper involved a unique population of Indigenous youth residing in the Peruvian Amazon, and found that (a) VIA virtues were differentially associated with well-being, (b) Humanity was a significant predictor across Cultural Citizenship and Social Engagement in the revised CYRM-21-P, and (c) higher resilience was correlated with higher well-being. Implications of this research can be used to inspire future research of Indigenous populations in a Latin American context to develop youth development programs that teach students from a strength-based perspective to improve vocational, academic, psychological, and social well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".