Linguistic factors and COVID-19 outcomes among long-term care residents in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic disproportionately affected frail individuals, especially those living in long-term care (LTC) homes. This study examined the role of linguistic factors on COVID-19 related outcomes in LTC homes. METHODS: We performed a population-based, retrospective cohort study of residents living in LTC homes in Ontario, Canada who were diagnosed with COVID-19 between March 31, 2020 and March 31, 2021. Resident language, obtained from LTC assessments, was used to classify residents into one of the three linguistic groups: Anglophone (English), Francophone (French), and allophone (other language). Language of the LTC home was determined using a person-time representation of the languages spoken by residents within each LTC home. We defined LTC facilities as French homes when Francophone residents contributed more than 25% of the person-days, and allophone homes when allophone residents contributed more than 50% of the person-days. Residents whose language corresponded to the language of the LTC home in which they were living were said to have received language-concordant care, while all other residents were said to have received language-discordant care. The outcomes of this study were ED visits, hospitalizations, and mortality within 90 days. RESULTS: We included a total of 26,829 LTC residents (20,315 Anglophones, 1,032 Francophones, and 5,482 allophones) living in 572 LTC homes (502 English, 28 French, 42 allophone) who were diagnosed with COVID-19. LTC residents who lived in language-discordant homes were more likely to have ED visits (adjusted HR 1.12, 95% CI 1.01-1.25) and hospitalizations (adjusted HR 1.15, 95% CI 1.02-1.29) when compared to LTC residents who lived in language-concordant homes. Residents-facility language discordance was not associated with overall mortality (adjusted HR 1.00, 95% CI 0.91-1.10) or in hospital mortality (adjusted HR 1.04, 95% CI 0.88-1.23). CONCLUSION: Residents living in language-discordant LTC facilities experienced more ED visits and hospitalizations following diagnosis of COVID-19. The findings of this study highlight the importance of providing frail, vulnerable individuals with linguistically concordant care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".