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End-of-life care in Toronto neonatal intensive care units: challenges for physician trainees

2013· article· en· W2094488769 on OpenAlexafffundabout
Manal F. El Sayed, Melissa Chan, Mary McAllister, Jonathan Hellmann

Bibliographic record

VenueArchives of Disease in Childhood Fetal & Neonatal · 2013
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsBC Children's HospitalHospital for Sick Children
FundersHospital for Sick Children
KeywordsTable (database)Intensive careMedicineFamily medicineNursingIntensive care medicineComputer scienceDatabase

Abstract

fetched live from OpenAlex

BACKGROUND: Physician trainees in neonatology can find it extremely challenging to care for patients from diverse linguistic and multicultural backgrounds. This challenge is particularly highlighted when difficult and ethically challenging end-of-life (EOL) decision-making with parents is required. While these interactions are an opportunity for growth and learning, they also have the potential to lead to misunderstanding and uncertainty and can add to trainees' insecurity, unpreparedness and stress when participating in such interactions. OBJECTIVES: To explore the challenges for trainees when EOL decisions are undertaken and to encourage them to reflect on how they might influence such decision-making. DESIGN AND INTERVIEW: An in-depth, semi-structured interview guide was developed: the interview questions address trainees' beliefs, attitudes, preferences and expectations regarding discussions of EOL neonatal care. Twelve interviews were completed and the audio records transcribed verbatim, after removal of identifying personal information. RESULTS: Participants identified six domains of challenge in EOL care: withdrawal of life-sustaining treatment based on poor outcome, explaining 'no resuscitation options' to parents, clarifying 'do not resuscitate (DNR)' orders, empowering families with knowledge and shared decision-making, dealing with different cultures and managing personal internal conflict. Participants experienced the most difficulty during the initial stages of training and eventually reported good knowledge of the EOL care process. They had a sense of security and confidence working within a multidisciplinary care team, which includes experienced nursing staff as well as bereavement and palliative care coordinator within the neonatal intensive care unit. CONCLUSIONS: The challenges experienced by physician trainees when providing EOL care can serve as focal points for improving EOL educational programmes for neonatal fellowship training.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.003
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.321
Teacher spread0.296 · 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 designQualitative
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

Citations28
Published2013
Admission routes3
Has abstractyes

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