End-of-Life Acute Care Use and Pain-Related Outcomes in Chinese-speaking Residents in Canadian Long-Term Care Homes
Bibliographic record
Abstract
OBJECTIVES: Patients from ethnically minoritized communities often face disparities in health care due to language and cultural barriers. This study aimed to compare health care use and end-of-life outcomes between Chinese-speaking residents living in language-concordant and language-discordant long-term care (LTC) homes. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: A total of 69,630 LTC residents who died between January 2017 and December 2019 in Ontario, Canada. METHODS: We compared Chinese-speaking residents in ethnic Chinese LTC homes (n = 931) (ie, language-concordant) with Chinese-speaking residents in non-Chinese homes (n = 510) (ie, language discordant), non-Chinese-speaking residents in ethnic Chinese homes (n = 408), and non-Chinese-speaking residents in all other homes (n = 67,781). Primary language spoken by the resident captured in the Resident Assessment Instrument-Minimum Data Set was used to classify residents as Chinese- or non-Chinese-speaking. Ethnic Chinese homes included those formally designated as a Chinese cultural home or where at least 20% of its residents spoke Chinese as their primary language. Main outcomes were hospitalization, emergency department visits, pain management in the last 3 days of life, and location of death. RESULTS: Residents in ethnic Chinese LTC homes, irrespective of their primary language, were significantly more likely to be admitted to hospitals in the last 3 days of life. Similarly, Chinese-speaking residents in all homes and all residents receiving care in ethnic Chinese homes were more likely to die in hospital than non-Chinese-speaking residents in all other homes. Chinese-speaking residents in language-concordant homes were less likely to report frequent and severe pain (odds ratio, 0.3; 95% CI, 0.2-0.7) than non-Chinese-speaking residents in other homes. CONCLUSIONS AND IMPLICATIONS: Chinese-speaking residents and residents in ethnic Chinese homes were more likely to be hospitalized at the end of life and die in hospitals. However, receiving care in a language-concordant environment was associated with lower odds of reporting frequent and severe pain near the end of life among Chinese-speaking LTC residents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".