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Record W6887733039 · doi:10.17605/osf.io/dtqyv

Epigenetic Investigations in the Family Intervention for Empowerment through Reading and Education (FIERCE) Study

2022· other· en· W6887733039 on OpenAlexaff

Bibliographic record

VenueOpen Science Framework · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEpigeneticsIntervention (counseling)Socioemotional selectivity theoryMental healthReading (process)DNA methylation

Abstract

fetched live from OpenAlex

This project is specifically for the DNA methylation and genotyping component of the FIERCE study of Syrian mother and child pairs of refugees in Jordan taking part in the We Love Reading (WLR) intervention. The current study will investigate whether and how the effect of the We Love Reading (WLR) intervention program gets “under the skin” of refugee children and their mothers in Jordan. To this end, we will examine differences in genetic and epigenetic markers (specifically, DNA methylation) between the intervention and control groups. Using an epigenetic biomarker of biological aging as well as an exploratory epigenome-wide approach, we will identify epigenetic changes associated with WLR. Post-hoc analysis will be conducted to scrutinize how biological findings may associate with underlying genotypic differences, as well as map onto children’s socioemotional outcomes and maternal mental health and wellbeing. We will further examine the role that family dynamics play in these aforementioned relationships and how family dynamics (specifically parenting and mental health) are reflected in DNA methylation as well.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.009
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.060
GPT teacher head0.414
Teacher spread0.353 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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