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Record W4394920409 · doi:10.1080/13623699.2024.2339732

They do their utmost: promise and limits of palliative care in two refugee camps in Rwanda, a qualitative study

2024· article· en· W4394920409 on OpenAlexaff
Sonya de Laat, Emmanuel Musoni, Kevin Bezanson, Rachel Yantzi, Olive Wahoush, Élysée Nouvet, Matthew Hunt, Takhliq Amir, Carrie Bernard, Christian Ntizimira, Lisa Schwartz

Bibliographic record

VenueMedicine Conflict & Survival · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsUniversity of TorontoWestern UniversityNOSM UniversityMcMaster UniversityMcGill UniversityImpact
FundersWellcome TrustWellcome
KeywordsRefugeePalliative careQualitative researchMedicineNursingSociologyPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

After often gruelling journeys, some refugees arrive at secure locations with severe injury or illness. Others find themselves shortly thereafter facing a life-limiting health condition. Palliative care has been the focus of recent research, and of academic and aid sector dialogue. In this study, we ask: What are experiences and needs of patients and care providers? What opportunities and obstacles exist to enhance or introduce means of reducing suffering for patients facing serious illness and injury in crisis settings? We present findings of a qualitative sub-study within a larger programme of research exploring moral and practical dimensions of palliative care in humanitarian crisis contexts. This paper presents vignettes about palliative care from refugees and care providers in two refugee camps in Rwanda, and is among the first to provide empirical evidence on first-hand experiences of individuals who have fled protracted conflict and face dying far from home. Along with narratives of their experiences, participants provided a range of recommendations from small (micro) interventions that are low cost, but high impact, through to larger (macro) changes at the systems and societal levels of benefit to policy developers and decision-makers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.114
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.078
GPT teacher head0.418
Teacher spread0.340 · 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 teacher head, 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

Citations3
Published2024
Admission routes1
Has abstractyes

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