Youth Conceptualization of Resilience Strategies in Four Low- and Middle-Income Countries
Bibliographic record
Abstract
The concept of resilience is increasingly influential in the development of interventions and services for young people, yet there is limited knowledge of how resilience-building strategies are conceptualized by young people across different cultures, particularly in low- and middle-income countries. The aim of this study was to capture 274 young people’s voices in disadvantaged communities in Kenya, Turkey, Pakistan, and Brazil through participatory research methods. Young people defined strategies in response to 4 adversity scenarios reflecting socioecological systems (young person, family, school, and community). Template analysis, underpinned by thematic design, was used to establish three broad themes of intrapersonal (self-management, cognitive re-appraisal, agency), interpersonal (social engagement, informal supports, formal supports), and religious resources. Proposed strategies were largely similar across the sites, with some contextual differences depending on the scenario (stressor) and cultural group. The findings support an ecological systems approach to resilience, which is consistent with the development of multimodal interventions for vulnerable youth and their families in disadvantaged communities in low- and middle-income countries.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".