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Record W2315318845 · doi:10.14288/1.0091865

The influence of the pediatric critical care culture on end-of-life decision making

2009· article· en· W2315318845 on OpenAlexaboutno aff
Tracie Northway

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsEnd-of-life careClinical decision makingMedicineIntensive care medicineNursingPalliative care

Abstract

fetched live from OpenAlex

The primary goal of the pediatric intensive care unit (PICU) health care team is to make critically ill children better. In many instances, the professionals working within the PICU come to understand that this goal is unachievable. Shifting the focus of care away from cure and toward comfort and a good death within the context of high technology and a focus on cure can be exceedingly difficult. The purpose of this study was to investigate the nature of pediatric critical care culture in Canada and its influences on end-of-life decision making for children for whom the possibility of cure is remote or non-existent. Guided by the qualitative method of ethnography, a rich and detailed description of the cultural influences of pediatric critical care on end-of-life decision making was obtained through semi-structured interviews with eleven PICU nurses and six PICU physicians from seven Canadian PICUs. Each of the participants had experience in caring for dying children and their families. Analysis focused on identifying cultural values and perspectives by comparing PICU cultures and the processes surrounding end-of-life decision making. The findings from this study suggest that practitioners value a sense of control over the PICU environment and end-of-life decision making. This need to control is apparent in how the dying process is managed. Physicians and nurses endeavour to orchestrate and plan for a child's death through the deliberate creation of a plan for managing the end of a child's life. Additional values and beliefs which influence end-of-life decision making focus on protecting the family and staff from emotional pain and suffering, valuing a "good quality of life", presenting a "united front", and maintaining loyalty to "the plan". Nurses describe experiencing emotional distress when requested to continue care aimed at cure when these efforts seemed futile. They describe feeling constrained within their practice to influence end-of-life decision making. Physicians describe a less emotional and more analytical response to demands for ongoing treatment under "unrealistic expectations". In essence, the unpredictability of death combined with the complexities of the pediatric critical care environment (e.g., technology, types and acuity of patient illnesses, access and flow issues, and nurse-family-physician relationship dynamics) create tremendous challenges for meeting the goals of a "planned death". This study is unique because it specifically addresses how the pediatric critical care culture influences end-of-life decision making. The findings of this study suggest a need to develop a deeper understanding of how the struggle to control dying in PICU impacts end-of-life decision making in order to improve upon the end-of-life experiences for dying children and their families. Further research and education are required that focus on: developing a better understanding of the concept, actualization and implications of control in a PICU; strategies to improve interdisciplinary end-of-life decision making within a family-centered care model; improving upon the current practices aimed at caring for dying children and their families in a non-hospice setting; developing strategies for individuals to explore their understanding and comfort with end-of-life care; and developing and sustaining collaborative relationships between health care professionals and families.

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)0.005
metaresearch head score (Gemma)0.021
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 score0.555
Threshold uncertainty score0.894

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.015
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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