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Record W2936812767 · doi:10.1080/10401334.2019.1598866

Training Pediatric Residents in Literacy Promotion: Residency Directors’ Perspectives

2019· article· en· W2936812767 on OpenAlexaboutno aff
Joanna Kinney, Manuel Jiménez, Lesley Mandel Morrow, Shilpa Pai

Bibliographic record

VenueTeaching and Learning in Medicine · 2019
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsPromotion (chess)LiteracyMedical educationMedicineIntervention (counseling)PsychologyNursingPedagogyPolitical science

Abstract

fetched live from OpenAlex

Phenomenon: The American Academy of Pediatrics and Canadian Pediatric Society recommend that pediatricians incorporate literacy promotion during well child care, but literacy promotion education during pediatric training remains understudied. We sought to understand how literacy promotion training is currently implemented in pediatric residency programs from the perspective of program directors. Approach: We conducted semistructured interviews with all 9 residency program directors in 1 state. We analyzed data iteratively coding transcripts using an immersion/crystallization approach to identify themes. Findings: We achieved saturation after 9 interviews with 11 participants. We identified 3 major themes: (a) Residency programs rely on an existing primary-care-based literacy promotion intervention (Reach Out and Read) and the resident continuity clinic for literacy promotion training; (b) program directors encourage early and repeated exposure to facilitate literacy promotion education; and (c) service obligations, content specifications, and pressure on faculty create competing time demands that function as key barriers to literacy promotion training. Insights: Residency program directors used an existing, widely used intervention and the infrastructure provided by continuity clinics to facilitate training on literacy promotion, a relatively new pediatric care standard. Additional work is needed to overcome the barriers identified by program directors.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.030
GPT teacher head0.356
Teacher spread0.326 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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