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Record W2256414328 · doi:10.1111/napa.12075

War‐affected children's approach to resettlement: Implications for child and family services

2015· article· en· W2256414328 on OpenAlexaffabout
Natasha Blanchet‐Cohen, Myriam Denov

Bibliographic record

VenueAnnals of Anthropological Practice · 2015
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill UniversityConcordia University
Fundersnot available
KeywordsPsychosocialKinshipService providerService (business)Service delivery frameworkDiversity (politics)Inclusion (mineral)Public relationsPsychologySociologyPolitical scienceGender studiesBusinessPsychiatry

Abstract

fetched live from OpenAlex

In Canada, the resettlement of thousands of war‐affected children every year poses new challenges to child and family services. Since young people arrive from multiple contexts either alone or accompanied by family or caregiver(s), after having endured significant trauma, stress, and adversity, conventional approaches to service delivery are seldom adequate. Drawing on anthropology of childhood literature, this paper calls for increased inclusion of young people's experiences and perspectives in reconfiguring psychosocial services. Interactive focus groups and in‐depth interviews with youth from war‐affected countries and service providers in Québec uncovered the ways that war alters family and how young people rely on both formal and informal support systems during resettlement. Young people and service providers reflected on inadequacies of current services in meeting the complex needs of youth while service professionals reported being ill‐equipped to support war‐affected youth. This paper posits that perspectives from the anthropology of childhood are critical in liaising between youth and professionals to provide services that build on a socioecological view of development, provide healing, and recognize the diversity of children and families’ kinship ties.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.496

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 head0.138
GPT teacher head0.459
Teacher spread0.322 · 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

Citations11
Published2015
Admission routes2
Has abstractyes

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