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Record W3089962027 · doi:10.4148/2161-4148.1059

The STRONG Resiliency Program for Newcomer Youth: A Mixed-Methods Exploration of Youth Experiences and Impacts

2020· article· en· W3089962027 on OpenAlexafffundabout
Claire V. Crooks, Nataliya Kubishyn, Maisha M. Syeda, Lynn Dare

Bibliographic record

VenueInternational Journal of School Social Work · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsWestern University
FundersStrongPublic Health AgencyPublic Health Agency of Canada
KeywordsPsychologyFocus groupCoping (psychology)Psychological interventionPositive Youth DevelopmentSocial connectednessPsychological resilienceQualitative researchApplied psychologyDevelopmental psychologyClinical psychologySocial psychologySociology

Abstract

fetched live from OpenAlex

Many refugee and immigrant youth face significant adversity, pre- and post-migration, as well as during their migratory journey. Although these youth demonstrate considerable resilience, there is also an opportunity to bolster coping skills and adjustment with group-based interventions in schools. We utilized a mixed-methods approach to describe the impacts of one such program, as experienced by youth (n=19). The program is a ten-session strengths-based resilience intervention that promotes relaxation skills, healthy coping, communication, and problem-solving. There is also one individual session focused on helping each participant share their journey narrative. Youth from six intervention groups participated in this study through completing pre- and post-intervention surveys and focus groups. Our qualitative results identified a high level of acceptability among youth. Perceived benefits included improved coping and relaxation strategies, increased confidence and trust, increased peer connectedness and belongingness, benefits of sharing and exchanging stories with peers, and increased knowledge in the Canadian context. Youths’ scores on resilience and use of STRONG skills increased significantly from pre- to post-intervention, but there was no change in school connectedness scores. We discuss the convergence between qualitative and quantitative findings and highlight some of the areas that were only evident in focus groups. Youth made minor suggestions for program improvement. Based on this small pilot, a resilience intervention resonated with newcomer youth and helped them foster their strengths.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.298
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.103
GPT teacher head0.454
Teacher spread0.352 · 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 teacher head, 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

Citations14
Published2020
Admission routes3
Has abstractyes

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