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Record W4367669348 · doi:10.1017/s0954579423000469

Multisystemic approaches to researching young people’s resilience: Discovering culturally and contextually sensitive accounts of thriving under adversity

2023· article· en· W4367669348 on OpenAlexaffabout
Michael Ungar, Linda Theron, Jan Höltge

Bibliographic record

VenueDevelopment and Psychopathology · 2023
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEmic and eticThrivingPsychologyPsychological resilienceDevelopmental psychologySocial psychologySociology

Abstract

fetched live from OpenAlex

As our understanding of the process of resilience has become more culturally and contextually grounded, researchers have had to seek innovative ways to account for the complex, reciprocal relationship between the many systems that influence young people's capacity to thrive. This paper briefly traces the history of a more contextualized understanding of resilience and then reviews a social-ecological model to explain multisystemic resilience. A case study is then used to show how a multisystemic understanding of resilience can influence the design and implementation of resilience research. The Resilient Youth in Stressed Environments study is a longitudinal mixed methods investigation of adolescents and emerging adults in communities that depend on oil and gas industries in Canada and South Africa. These communities routinely experience stress at individual, family, and institutional levels from macroeconomic factors related to boom-and-bust economic cycles. Building on the project's methods and findings, we discuss how to create better studies of resilience which are able to capture both emic and etic accounts of positive developmental processes in ways that avoid the tendency to homogenize children's experience. Limitations to doing multisystemic resilience research are also highlighted, with special attention to the need for further innovation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.093
GPT teacher head0.362
Teacher spread0.268 · 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 designObservational
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

Citations42
Published2023
Admission routes2
Has abstractyes

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