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Record W1277589343 · doi:10.3233/prm-150327

Complex care core curriculum for pediatric post-graduate trainees: Results of a North American needs assessment

2015· article· en· W1277589343 on OpenAlexaffabout
Anne Marie Sbrocchi, Catherine Millar, Catherine Hénin, Manon Allard, Hema Patel

Bibliographic record

VenueJournal of Pediatric Rehabilitation Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsMcGill University
Fundersnot available
KeywordsCurriculumMedical educationCore competencyNeeds assessmentHealth carePsychologyCore curriculumMedicineNursingPedagogy

Abstract

fetched live from OpenAlex

PURPOSE: There are increasing numbers of children with chronic, complex conditions requiring comprehensive care, however post-graduate training in this field is limited. Lack of training may contribute to reticence in engaging with their care. Although children with medical complexity (CMC) are heterogeneous, their care needs are similar. We aimed to address a lack of explicit training via a standardized curriculum. As an initial step, a collaborative needs assessment of key content was developed. METHODS: An on-line survey with drafted learning objectives was sent to professionals skilled in complex care. Participants indicated if an objective should be obtained by the end of Junior residency (PGY2), Senior residency (PGY4), or not at all. RESULTS: Eighty-two Canadian and US professionals participated; 60.3% practiced in a University Health Centre and 60% cared for CMC and participated in pediatric postgraduate education. Over 80% felt that the medical home concept should be understood by the end of PGY2, and that trainees should develop a care plan by the end of PGY4. CONCLUSION: This needs assessment supported the learning objectives and provided information on the expected competency time line for knowledge and skill acquisition. Additional comments will be used to revise the objectives.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.235
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
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 head0.078
GPT teacher head0.386
Teacher spread0.308 · 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

Citations11
Published2015
Admission routes2
Has abstractyes

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