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Record W2763150725 · doi:10.1093/pch/19.6.e35-92

94: An Organizational Ethics Model of Roles, Challenges, and Quality Indicators of End-of-Life Care in the Neonatal Intensive Care Unit (NICU)

2014· article· en· W2763150725 on OpenAlexaffabout
Charmaine C. Williams, J Gibson, Janice Cairnie, A. Laupacis, Haresh Kirpalani

Bibliographic record

VenuePaediatrics & Child Health · 2014
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNursingThematic analysisContext (archaeology)End-of-life careHealth careSituational ethicsPsychologyHarmNeonatal intensive care unitMedicinePalliative careQualitative researchSocial psychology

Abstract

fetched live from OpenAlex

Quality of care during decision-making and at end-of-life influences how parents cope with neonatal death. Associated moral distress affects individuals and an organization's ethical climate. To explore the challenges of meeting parents' and infants' needs in end-of-life care and decision-making in the NICU. Thirty-six semi-structured interviews were undertaken with 43 health care workers (HCW) from one tertiary Canadian NICU: 23 nurses, five neonatologists, five residents, three social workers, two fellows, two nurse practitioners, two chaplains, and one dietician. Questions (developed by a multidisciplinary research team including parents) probed for HCW's experiences, perception of parent needs, and challenges to providing quality end-of-life care. Recorded interviews were transcribed, thematic analysis performed with triangulation of themes across investigators, and an organizational ethics model of end-of-life care evolved. Figure 1 depicts an organizational model of roles, challenges, and quality indicators of end-of-life care based on HCW themes. The ability to meet parents' and infants' needs were influenced by intrinsic and extrinsic factors related to HCW's roles and perception of team function. These were influenced by prior experiences and training, personal and societal values, and situational context. Similarly, indicators of quality care were both intrinsic (self-satisfaction) and extrinsic (parent feedback). Consequences of poor quality of care resulted in ‘harm’ to both families and HCWs. Finally, lack of institutional supports and resources were barriers to care (Figure 1). An organizational model identifying HCW roles and challenges in providing quality end-of-life care was developed through stakeholder interviews. This model may guide institutional identification of quality indicators and inform the development of policies and programs aimed toward improving end-of life care practices.

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)0.009
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.015
Scholarly communication0.0080.003
Open science0.0020.003
Research integrity0.0020.002
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 head0.134
GPT teacher head0.456
Teacher spread0.322 · 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 designTheoretical or conceptual
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
Published2014
Admission routes2
Has abstractyes

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