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Record W2947063890 · doi:10.1177/1757913919846531

Risk factors and mitigation of influenza among Indigenous children in Australia, Canada, United States, and New Zealand: a scoping review

2019· review· en· W2947063890 on OpenAlexaffabout
Catherine Mcleod, Nikesh Adunuri, Richard Booth

Bibliographic record

VenuePerspectives in Public Health · 2019
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsIndigenousCircumpolar starEnvironmental healthMedicinePublic healthMetisInclusion (mineral)GeographyNursingPsychology

Abstract

fetched live from OpenAlex

AIM: This review considers prominent risk factors and mitigation strategies of influenza among Indigenous children. METHODS: Seven electronic databases were searched from the period of 2004-2017 to locate articles discussing influenza among Indigenous children in the developed circumpolar nations of Australia, Canada, United States, and New Zealand. Articles selected for inclusion discussed influenza among Indigenous children as either individuals or as a part of a community. Ancestry searches of articles meeting the review criteria were also undertaken to discern seminal research in this topic area. RESULTS: From the 39 primary research studies included, marked risk factors and mitigation strategies of influenza among Indigenous children were identified using inductive analysis. Notable risk factors included age under 2 years, cigarette smoke exposure, presence of a chronic illness, and crowded living conditions. Successful mitigation of influenza for Indigenous children included strategies to improve vaccine coverage, provision of health education, and policy change. CONCLUSION: In the past, the impact of influenza upon Indigenous communities has been devastating for both children and their families. By utilizing existing public health infrastructure and collaborating with culturally unique Indigenous groups, preventive action for Indigenous children at significant risk of contracting influenza can be realized.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.335
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.179
GPT teacher head0.456
Teacher spread0.276 · 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.

Study designObservational
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

Citations7
Published2019
Admission routes2
Has abstractyes

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