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Record W3011131819 · doi:10.1136/bmjpo-2019-000615

Asylum seeking children and adolescents in Australian immigration detention on Nauru: a longitudinal cohort study

2020· article· en· W3011131819 on OpenAlexaff
Karen Zwi, Louise Sealy, Nora Samir, Nan Hu, Reza Rostami, Rishi Agrawal, Sarah Cherian, Jacinta Coleman, Joshua Francis, Hasantha Gunasekera, David Isaacs, Penny Larcombe, David Levitt, Sarah Mares, Raewyn Mutch, Louise Newman, Shanti Raman, Helen Young, Christy Norwood, Raghu Lingam

Bibliographic record

VenueBMJ Paediatrics Open · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsRoyal Ottawa Mental Health Centre
FundersUniversity of New South Wales
KeywordsImmigration detentionImmigrationCohortLongitudinal studyMedicineDemographyPsychologyPsychiatryPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

INTRODUCTION: Immigration detention has a profound and negative impact on the physical health, mental health, development and social-emotional well-being of children, adolescents and their families. Australian clinicians will report results from detailed health and well-being assessments of asylum seeking children and adolescents who have experienced prolonged immigration detention. METHODS AND ANALYSIS: This is a national, multicentre study with a longitudinal cohort design that will document health and well-being outcomes of the children and adolescents who have been detained in offshore detention on the remote island of Nauru. Outcome measures will be reported from the time arrival in Australia and repeated over a 5-year follow-up period. Measures include demographics, residency history and refugee status, physical health and well-being outcomes (including mental health, development and social-emotional well-being), clinical service utilisation and psychosocial risk and protective factors for health and well-being (eg, adverse childhood experiences). Longitudinal follow-up will capture outcomes over a 5-year period after arrival in Australia. Analysis will be undertaken to explore baseline risk and protective factors, with regression analyses to assess their impact on health and well-being outcomes. To understand how children's outcomes change over time, multilevel regression analysis will be utilised. Structural equation modelling will be conducted to explore the correlation between baseline factors, mediational factors and outcomes to assess trajectories over time. ETHICS AND DISSEMINATION: This research project was approved by the Sydney Children's Hospitals Network Human Research Ethics Committee. Subsequent site-specific approvals have been approved in 5 of the 11 governing bodies where the clinical consultations took place. In order to ensure this research is relevant and sensitive to the needs of the cohort, our research team includes an asylum seeker who has spent time in Australian immigration detention. Results will be presented at conferences and published in peer-reviewed Medline-indexed journals.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.128
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.362
Teacher spread0.320 · 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 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

Citations12
Published2020
Admission routes1
Has abstractyes

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