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Record W3135348556 · doi:10.1186/s12904-021-00768-5

Decision making in the end-of-life care of patients who are terminally ill with cancer – a qualitative descriptive study with a phenomenological approach from the experience of healthcare workers

2021· article· en· W3135348556 on OpenAlexfundno aff
Angela Luna-Meza, Natalia Godoy-Casasbuenas, Jose Andrés Calvache, Eduardo Díaz Amado, Fritz Eduardo Gempeler Rueda, Olga Milena García Morales, F Gustavo Leal, Carlos Gómez–Restrepo, Esther de Vries

Bibliographic record

VenueBMC Palliative Care · 2021
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
FundersQueen's UniversityQueen's University BelfastInstituto Nacional de CancerologíaPontificia Universidad JaverianaDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
KeywordsPalliative careEnd-of-life careAdvance care planningMedicinePsychological interventionNursingQualitative researchHealth careContext (archaeology)Exploratory researchPsychologyFamily medicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: In Colombia, recent legislation regarding end-of-life decisions includes palliative sedation, advance directives and euthanasia. We analysed which aspects influence health professionals´ decisions regarding end-of-life medical decisions and care for cancer patients. METHODS: Qualitative descriptive-exploratory study based on phenomenology using semi-structured interviews. We interviewed 28 oncologists, palliative care specialists, general practitioners and nurses from three major Colombian institutions, all involved in end-of-life care of cancer patients: Hospital Universitario San Ignacio and Instituto Nacional de Cancerología in Bogotá and Hospital Universitario San José in Popayan. RESULTS: When making decisions regarding end-of-life care, professionals consider: 1. Patient's clinical condition, cultural and social context, in particular treating indigenous patients requires special skills. 2. Professional skills and expertise: training in palliative care and experience in discussing end-of-life options and fear of legal consequences. Physicians indicate that many patients deny their imminent death which hampers shared decision-making and conversations. They mention frequent ambiguity regarding who initiates conversations regarding end-of-life decisions with patients and who finally takes decisions. Patients rarely initiate such conversations and the professionals normally do not ask patients directly for their preferences. Fear of confrontation with family members and lawsuits leads healthcare workers to carry out interventions such as initiating artificial feeding techniques and cardiopulmonary resuscitation, even in the absence of expected benefits. The opinions regarding the acceptability of palliative sedation, euthanasia and use of medications to accelerate death without the patients´ explicit request vary greatly. 3. Conditions of the insurance system: limitations exist in the offer of oncology and palliative care services for important proportions of the Colombian population. Colombians have access to opioid medications, barriers to their application are largely in delivery by the health system, the requirement of trained personnel for intravenous administration and ambulatory and home care plans which in Colombia are rare. CONCLUSIONS: To improve end-of-life decision making, Colombian healthcare workers and patients need to openly discuss wishes, needs and care options and prepare caregivers. Promotion of palliative care education and development of palliative care centres and home care plans is necessary to facilitate access to end-of-life care. Patients and caregivers' perspectives are needed to complement physicians' perceptions and 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)0.008
metaresearch head score (Gemma)0.009
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 score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.010
Scholarly communication0.0050.005
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.201
GPT teacher head0.446
Teacher spread0.245 · 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

Citations80
Published2021
Admission routes1
Has abstractyes

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