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Record W2920208862 · doi:10.1080/07481187.2018.1526829

The experiences of physicians, nurses, and social workers providing end-of-life care in a pediatric acute-care hospital

2019· article· en· W2920208862 on OpenAlexaff
Barbara Muskat, Andrea Greenblatt, Samantha J. Anthony, Laura Beaune, Pam Hubley, Christine Newman, David Brownstone, Adam Rapoport

Bibliographic record

VenueDeath Studies · 2019
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsNursingAcute careCoping (psychology)MedicineQualitative researchSocial workPromotion (chess)End-of-life careHealth carePsychologyFamily medicinePalliative careClinical psychology

Abstract

fetched live from OpenAlex

This qualitative study explored the experiences of social workers, nurses, and physicians providing end-of-life care to children in a pediatric acute-care hospital setting. Findings demonstrated that participants experienced both professional and personal impacts of their work and employed various coping strategies under each of these domains. The acute-care setting was found to create unique challenges in providing end-of-life care. Implications for policy and practice include promotion of both individual and institutional-level coping strategies and supports that meet the various needs of staff. Implications for future research include a nuanced examination of differences in experiences among nurses, social workers, and physicians.

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)0.006
metaresearch head score (Gemma)0.010
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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.008
Scholarly communication0.0030.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.381
Teacher spread0.332 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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