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Record W4396893235 · doi:10.1097/or9.0000000000000132

Trauma-informed palliative care for humanitarian crises

2024· article· en· W4396893235 on OpenAlexaff
Janet de Groot, Danielle S. Miller, Kelcie Willis, Tamara Green, Lynn Calman, Andrea Feldstain, Seema Rajesh Rao, Ozan Bahçivan, Dwain C. Fehon

Bibliographic record

VenueJournal of Psychosocial Oncology Research and Practice · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPalliative careMedicineMedical emergencyNursingPsychology

Abstract

fetched live from OpenAlex

Abstract Healthcare triage during humanitarian crises requires attention to saving lives and prevention of suffering at end of life. The prevalence of life-threatening experiences during humanitarian crises needs a trauma-informed palliative care approach, attending to the trauma-related psychosocial needs of patients, caregivers, and health care providers to support healing. This commentary includes research and practice literature that builds on and complements themes from an International Psychosocial Oncology Society Palliative Care Special Interest Group initiative. During humanitarian crises, palliative care experts contribute to mobilizing and training host country health care providers and volunteers who reciprocally promote cultural sensitivity for patients and their caregivers in all aspects of death and dying. Future directions require assessing how best to integrate trauma-informed principles into early and later responses to humanitarian crises. Culturally sensitive research partnerships with patients and caregivers must account for hierarchy and flexibility in research design and knowledge construction.

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)0.006
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.005
Scholarly communication0.0020.003
Open science0.0010.005
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.462
GPT teacher head0.675
Teacher spread0.213 · 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
Published2024
Admission routes1
Has abstractyes

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