Race and birth country are associated with discharge location from hospital: A retrospective cohort study of demographic differences for patients receiving inpatient palliative care
Bibliographic record
Abstract
Background While past studies investigated access to palliative care among marginalized groups, few assessed whether there are differences in clinical process indicators based on demographics among those receiving palliative care. We aimed to: describe demographics among patients receiving inpatient palliative care; and evaluate whether demographic variables are associated with differences in disposition (i.e., discharge location), length of stay (LOS), and timing of inpatient palliative care referral and consultation. Methods Retrospective cohort study using electronic medical record data to study patients seen by inpatient palliative care at Mount Sinai Hospital in Toronto, Canada between April 2018 to March 2019. Primary outcome was disposition. Secondary outcomes were LOS, time from admission to palliative referral, and time from referral to consultation. We summarized quantitative data descriptively and used fisher exact tests to explore relationships between categorial variables. For continuous outcomes, we ran one-way ANOVA tests. Findings A total of 187 patients were referred to palliative care and met inclusion criteria. Mean age was 68·8 and 55·6% were female. 46·7% were born in Canada, 58·2% were White and 78·4% preferred English communication. Variables significantly associated with disposition were: birth country ( p = 0·04), and race/ethnicity ( p = 0·03). Language (F ratio = 3·6, p = 0·004) was significantly associated with time from admission to palliative care referral. No variables were associated with LOS or time from referral to consult. Interpretation Inequalities in disposition, and how long it takes to refer to palliative care may exist. Further studies should focus on understanding the underlying practices that constructed, and maintained these inequalities in care. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".