Training Pediatric Residents in Literacy Promotion: Residency Directors’ Perspectives
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".