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Record W2984427675 · doi:10.1080/21683603.2019.1660284

Promoting child and youth resilience by strengthening home and school environments: A literature review

2019· review· en· W2984427675 on OpenAlexaff
Akwasi Twum-Antwi, Philip Jefferies, Michael Ungar

Bibliographic record

VenueInternational Journal of School & Educational Psychology · 2019
Typereview
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPerspective (graphical)Resilience (materials science)Psychological interventionPsychological resiliencePsychologyDevelopmental psychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

A multisystemic model of resilience suggests that the capacity of one system to cope with atypical stress improves the capacity of co-occurring systems. In this paper, we review research demonstrating this relationship, where the more resilient caregivers are, the more likely children are to experience the promotive and protective factors they require for optimal growth and development in both home and school settings. We examine research from the last two decades on school- or family-based resilience promoting interventions, and advocate for a new perspective which adopts a multisystemic view of resilience in order to redirect the focus of the international research agenda, which places emphasis on children rather than systems. The implications of this multisystemic approach to resilience are discussed in relation to the design of programs that promote the well-being of parents and teachers in ways that contribute to more supportive and stable home and school environments for children.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.035
GPT teacher head0.448
Teacher spread0.413 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations125
Published2019
Admission routes1
Has abstractyes

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