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Record W2008965683 · doi:10.1002/hast.384

Innovative Holistic Teaching in a Canadian Neonatal Perinatal Residency Program

2014· article· en· W2008965683 on OpenAlexfundaboutno aff
Thierry Daboval, Emanuela Ferretti, Gregory P. Moore

Bibliographic record

VenueThe Hastings Center Report · 2014
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsnot available
FundersUniversity of Ottawa
KeywordsMedical educationCommunication skillsPerinatal medicineMedical ethicsHealth carePsychologyMedicineNursingPregnancyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Ethically complex and challenging cases confront health care professionals in neonatal‐perinatal medicine more often than in most other subspecialties in medicine. Neonatologists regularly encounter situations where crucial life‐or‐death decisions need to be made in the best interest of an infant and its family. While physicians and their professional societies seem to dictate this best interest standard by weighing the risk of mortality and morbidities, parents may have other perspectives to be considered . Our review of programs for teaching ethics in Canadian neonatal‐perinatal residency programs has revealed that 90 percent of them incorporated formal and informal medical ethics education, meeting the Royal College of Physician and Surgeons of Canada requirements, but that the teaching strategies, topics covered, and time devoted to teaching ethics are not standardized. Lectures and case presentations—the pedagogic strategy used by most programs—are not ideal for teaching communication skills. We propose, therefore, a holistic approach to teaching and training that imparts (1) a traditional understanding of ethical theory and reasoning, (2) advanced skills in communication and counseling, and (3) a disposition to engage in self‐reflection and to be aware of the emotional and spiritual dimensions of neonatal‐perinatal medicine .

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.382
Teacher spread0.341 · 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
Published2014
Admission routes2
Has abstractyes

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