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Record W4403680521 · doi:10.1093/pch/pxae067.057

58 A hospital paediatrics education curriculum: Quality improvement for inpatient paediatric learner education on CTU

2024· article· en· W4403680521 on OpenAlexaboutno aff
Sabine L. Laguë, Mia Remington

Bibliographic record

VenuePaediatrics & Child Health · 2024
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumMedical educationQuality (philosophy)MedicineFamily medicinePsychologyPediatricsPedagogy

Abstract

fetched live from OpenAlex

Abstract Background Paediatric learners experience the bulk of their inpatient medicine on the wards. While bedside teaching is invaluable, ward learning can be impacted by seasonal exposure, clinical homogeneity, and volume. Locally our learners requested more formal ward teaching to encourage dialogue around common topics perhaps not encountered on rotation. We queried whether having a database of interactive guideline-based educational experiences would minimize barriers (eg. time, resources) to providing formal inpatient learner education. Objectives We sought to enhance general paediatrics ward teaching by developing a curriculum of guideline-based interactive educational content on common inpatient paediatric topics. Design/Methods This study was conducted at a Canadian tertiary care paediatric hospital with a range of learners (MSI3, MSI4, paediatric residents (R1-R4)) over 12 months. 1) Needs Assessment: We surveyed residents, hospitalist fellows, and CTU physicians regarding a) perceived need for a curriculum, b) content (top 5 CPS guidelines and top 5 non-CPS topics), c) teaching modality, d) need for handouts, and e) curriculum structure. 2) Curriculum generation: Curriculum was developed according to needs assessment results. 3) Quality improvement: Learners were surveyed after each session with four Likert scale questions (5=strongly agree) regarding teaching quality, clinical translatability, enhanced knowledge/understanding, and improved clinical confidence. Results 1) Needs assessment: Needs assessment (N=25) unanimously supported a structured curriculum. Eleven CPS statements and twelve non-CPS guideline-based topics were identified for content. Three preferred teaching formats were highlighted, and handouts were important to 88% of respondents. The curriculum was favoured to be 50-75% standardized. 2) Curriculum generation: We developed a curriculum of 13 topics with different delivery modalities (e.g. case-based powerpoints, whiteboard talks, Jeopardy, simulation). All were guideline-based, interactive, and had fill-in-the-blank learner handouts. The curriculum was standardized with 4/6 CTU teaching sessions per block being derived from the curriculum bank, and the remainder being at educator discretion (e.g. curriculum bank, interesting case). 3) Quality improvement: Likert scales (5=strongly agree) were positive from all learners (MSI3-R4; N=52), with scores of 4.9+/-0.2 for teaching quality, translatability to practice, and improved understanding, and 4.8+/-0.3 for improved clinical confidence. Conclusion Here we present an interactive and guideline-based paediatric inpatient learner education curriculum whose development, from content to delivery modality, was directed by community needs. We are now seeking funding to prepare for national distribution (e.g. peer-review, licensure), and intend to publish on platform where the curriculum would be downloadable for use by all educators (i.e. senior residents, staff).

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)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.352
Teacher spread0.340 · 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
Published2024
Admission routes1
Has abstractyes

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