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Record W2074127757 · doi:10.1155/2012/820290

Risks to Early Childhood Health and Development in the Postconflict Transition of Northern Uganda

2012· article· en· W2074127757 on OpenAlexafffund
Theresa McElroy, Stella Atim, Charles P. Larson, Robert W. Armstrong

Bibliographic record

VenueInternational Journal of Pediatrics · 2012
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersUniversity of British ColumbiaInternational Development Research CentreBC Children's HospitalChild and Family Research Institute
KeywordsMedicinePsychological interventionObservational studyEarly childhoodFocus groupPediatricsDevelopmental psychologyEnvironmental healthNursingPsychology

Abstract

fetched live from OpenAlex

Research from numerous fields of science has documented the critical importance of nurturing environments in shaping young children's future health and development. We studied the environments of early childhood (birth to 3 years) during postconflict, postdisplacement transition in northern Uganda. The aim was to better understand perceived needs and risks in order to recommend targeted policy and interventions. Methods. Applied ethnography (interview, focus group discussion, case study, observational methods, document review) in 3 sites over 1 year. Results. Transition was a prolonged and deeply challenging phase for families. Young children were exposed to a myriad of risk factors. Participants recognized risks as potential barriers to positive long-term life outcomes for children and society but circumstances generally rendered them unable to make substantive changes. Conclusions. Support structures were inadequate to protect the health and development of children during the transitional period placing infants and young children at risk. Specific policy and practice guidelines are required that focus on protecting hard-to-reach, vulnerable, children during what can be prolonged and extremely difficult periods of transition.

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 score0.134
Threshold uncertainty score0.199

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 head0.028
GPT teacher head0.313
Teacher spread0.285 · 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

Citations5
Published2012
Admission routes2
Has abstractyes

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