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Record W4381938146 · doi:10.1093/jbcr/irad045.168

573 The Impact of Burn Injuries on Indigenous Populations

2023· article· en· W4381938146 on OpenAlexaffabout
Vincent Gabriel, Leah Verburg, Graham McCaffrey

Bibliographic record

VenueJournal of Burn Care & Research · 2023
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIndigenousMedicinePopulationCINAHLDisfigurementEthnic groupMetisPoison controlFamily medicineMedical emergencyPsychological interventionSurgeryNursingEnvironmental healthAnthropology

Abstract

fetched live from OpenAlex

Abstract Introduction Racial and ethnic minorities experience disparities in prevention and treatment of burn injury. However, research on burn injuries in Indigenous populations is limited. This review summarizes literature on burn injuries in Indigenous populations that require consideration due to systemic racism, intergenerational trauma, and health disparities to inform new research. Methods A search was conducted in CINAHL, Ovid MEDLINE, PSYCinfo, and SocINDEX. for “burn OR scars OR scald OR deformity OR disfigurement” and “Aboriginal OR Indigenous OR First Nation OR American Indian OR Maori OR Native OR Torres Strait Islander OR Amerindian OR Inuit OR Metis OR Pacific Islander”. Search criteria were set by the authors with librarian assistance. Inclusion 1) peer-reviewed studies of burns in Indigenous persons 2) in English. Exclusion 1) no data specific to Indigenous burns 2) not peer-reviewed 3) not in full text 4) protocol publications. Selection was performed by a member of the research team and reviewed by the co-authors. Results The search identified 1,091 studies with 51 for review. Fifteen were excluded. Three were editorials or letters. Five were not full text. One was a protocol only, and one did not reference Indigenous persons. Two reported accident rates in Indigenous populations but not burns data. Two excluded articles did not report burns data in the Indigenous population. One was excluded as the conclusions were not supported by the data. Included publications were published between 1987 and 2021 with 24 between 2011 - 2021. Indigenous populations have a rate of burn injury up to two times of the general population in the US, Canada, New Zealand, and Australia. Indigenous people suffered more injuries from flame and inhalation. Burns in Indigenous people are of greater total body surface area and deeper. Indigenous persons had longer lengths of stay, more complications, and need for additional surgeries. The Indigenous population in the US is associated with more hypertrophic scarring. Indigenous patients in Australia struggle with a lack of culturally safe communication and support for aftercare. Conclusions Current literature is limited specific to burns in Indigenous populations. Racial disparities exist in burn injury incidence and outcomes for Indigenous persons. Indigenous populations suffer burn injuries, have more complications, and longer lengths of stay in hospital. Current care practices lack cultural safety considerations. Applicability of Research to Practice Qualitative research in this area will help providers better understand the experiences of Indigenous burn patients to develop more culturally competent care. We are currently developing a study using qualitative hermeneutic methodology to learn about the experiences of Indigenous burn survivors’ injuries, recovery, and social reintegration.

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)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.006
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.001

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 head0.144
GPT teacher head0.488
Teacher spread0.344 · 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 designObservational
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
Published2023
Admission routes2
Has abstractyes

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