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Record W3164888594 · doi:10.1136/bmjopen-2020-042978

Association between end-of-life cancer care and immigrant status: a retrospective cohort study in Ontario, Canada

2021· article· en· W3164888594 on OpenAlexafffundabout
Anna Chu, Lisa Barbera, Rinku Sutradhar, Urun Erbas Oz, Erin O’Leary, Hsien Seow

Bibliographic record

VenueBMJ Open · 2021
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of CalgaryMcMaster UniversityAlberta Health ServicesPublic Health OntarioUniversity of Toronto
FundersBC Cancer AgencyCanadian Cancer Society Research InstituteOntario Ministry of Health and Long-Term CareCanadian Centre for Applied Research in Cancer ControlInstitute for Clinical Evaluative SciencesCancer Care Ontario
KeywordsMedicineImmigrationDemographyEthnic groupRetrospective cohort studyPalliative careGerontologyEnd-of-life carePopulationLogistic regressionCohortCohort studyOdds ratioFamily medicineEnvironmental healthNursingInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare recent immigrants and long-term residents in Ontario, Canada, on established health service quality indicators of end-of-life cancer care. DESIGN: Retrospective, population-based cohort study of cancer decedents between 2004 and 2015. SETTING: Ontario, Canada. PARTICIPANTS: We grouped 13 085 immigrants who arrived in Ontario in 1985 or later into eight major ethnic groups based on birth country, mother tongue and surname, and compared them to 229 471 long-term residents who were ≥18 years at the time of death. PRIMARY AND SECONDARY OUTCOME MEASURES: Aggressive care, defined as a composite of ≥2 emergency department visits, ≥2 new hospitalisations or an intensive care unit admission within 30 days of death; and supportive care, defined as a physician house call within 2 weeks, or palliative nursing or personal support worker home visit within 6 months of death. Multivariable logistic regression was used to examine the association between immigration status and the odds of each main outcome. RESULTS: Compared with long-term residents, immigrants overall and by ethnic group had higher rates of aggressive care (13.7% vs 17.5%, respectively; p<0.001). Among immigrants, Southeast Asians had the highest use while White-Eastern and Western Europeans had the lowest. Supportive care use was similar between long-term residents and immigrants (50.0% vs 50.5%, respectively; p=0.36), though lower among Southeast Asians (46.6%) and higher among White-Western Europeans (55.6%). After adjusting for sociodemographic characteristics and comorbidities, immigrants remained more likely than long-term residents to receive aggressive care (OR: 1.15, 95% CI 1.09 to 1.21), yet were less likely to receive supportive care (OR: 0.95, 95% CI 0.91 to 0.98). CONCLUSIONS: Among cancer decedents in Ontario, immigrants are more likely to use aggressive healthcare services at the end of life than long-term residents, while supportive care varies by ethnicity. Contributors to variation in end-of-life care require further study.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.395
Teacher spread0.347 · 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

Citations20
Published2021
Admission routes3
Has abstractyes

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