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Record W4384926148 · doi:10.1002/jcop.23079

Resilience in children and youth in street situations in León, Nicaragua

2023· article· en· W4384926148 on OpenAlexaff
Kayla Hamel, Yvonne Bohr

Bibliographic record

VenueJournal of Community Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsYork University
Fundersnot available
KeywordsContext (archaeology)Focus groupPsychological resiliencePsychological interventionResilience (materials science)Family resilienceGrounded theoryPsychologySociologyQualitative researchAgency (philosophy)Flexibility (engineering)Social psychologyGeographyManagementSocial science

Abstract

fetched live from OpenAlex

There are tens of millions of children and youth in street situations (CYSS) worldwide, the majority of whom are males living in low- and middle-income countries. Many of these children demonstrate impressive adaptability and resilience. The focus of the current research was on the resilience of male CYSS in León, Nicaragua. Qualitative data were collected through individual interviews and focus groups with CYSS, their family members, community members, and staff of a local nonprofit, with the objective of exploring and consolidating local understandings of resilience. Grounded theory analysis of qualitative data yielded a context-specific conceptual model of resilience as it pertains to CYSS in León. Six qualities were identified to represent the experience of resilience in this group: agency, belonging, flexibility, protection, self-regulation, and self-worth. The knowledge generated from this research can serve as a foundation to develop and implement resilience-promoting interventions for CYSS.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.334

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.067
GPT teacher head0.448
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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