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Record W4413214243 · doi:10.5737/23688076352361

Moral distress among family caregivers of people with cancer in palliative care: A qualitative study

2025· article· en· W4413214243 on OpenAlexvenueno aff
Ana Lucía Oliveros Rozo, Milena Schneiders, Mateus Rodrigo Palombit, Kassiano Carlos Sinski, Rafael Luis Moura Lima do Carmo, Ana Cristina Lauer Garcia, Jeferson Santos Araújo, Júlia Valéria de Oliveira Vargas Bitencourt, Rosana Aparecida Spadoti Dantas, Vander Monteiro da Conceição

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2025
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisDistressPalliative careQualitative researchFamily memberPsychologyFamily caregiversTheme (computing)End-of-life careDiseaseMedicineClinical psychologyNursingFamily medicineSociology

Abstract

fetched live from OpenAlex

The study aimed to understand the triggering events of moral distress according to the family caregivers of people with cancer in palliative care. This is a longitudinal qualitative approach study, using the concept of moral distress as an interpretative reference. Ten family caregivers participated in in-depth interviews. The data were analyzed according to the inductive thematic analysis technique, and two themes were identified. In the first theme, entitled "The repercussions following diagnosis," participants reported the uncertainties they experienced following their family member's cancer diagnosis. In the second theme, entitled "The transformation of daily life", participants expressed how providing care for the family member with cancer changed their daily lives. While experiencing the role of a caregiver, they faced dilemmas and uncertainties that led them to feel moral distress, since this situation will follow them continuously until the outcome of the disease.

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)0.011
metaresearch head score (Gemma)0.022
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 score0.013
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0130.008
Scholarly communication0.0030.003
Open science0.0010.004
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 head0.079
GPT teacher head0.536
Teacher spread0.457 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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