Access to palliative care services for limited English proficient patients with advanced NSCLC.
Bibliographic record
Abstract
99 Background: More than a quarter of people living in British Columbia, Canada speak languages other than English in their homes. Immigrants often encounter communication challenges with their health care providers (HCPs), have poor health literacy, and have a limited understanding on navigating the health care system. NSCLC patients with limited English proficiency (LEP) may receive less palliative care services despite high symptom burden and significant needs due to these factors. The study goals were to observe the difference in access to community palliative home care (CPHC) and rate of completing a Do Not Resuscitate (DNR) form between NSCLC patients who are English proficient (EP) and LEP. Methods: All patients with advanced NSCLC referred to BC Cancer–Vancouver Centre in 2016 and received medical care were included (N=176). Patients seen with a medical interpreter were considered to be LEP. Demographics and clinical information were collected retrospectively. UVA using X2 test and Fisher’s exact test were used to compare EP and LEP patients. Mann-Whitney test was used to compare the median time from CPHC referral and signed DNR to death between EP and LEP patients. Results: Language of communication: English 65%, Cantonese 22%, Mandarin 6%, Korean 1%, Tagalog 1%, and other 5%. Baseline characteristics: median age 69 EP vs 76 LEP, female 44% EP vs 65% LEP, non-squamous 68% EP vs 72% LEP and squamous 14% EP vs 6% LEP. There was no difference in the rate of CPHC referral (87% EP vs 80% LEP, p=0.342) and signed DNR form (92% EP vs 89% LEP, p=0.549). The median time from CPHC referral to death was 10 weeks EP vs 15 weeks LEP (p=0.039). The median time from signed DNR to death was 5 weeks EP vs 6 weeks LEP (p=0.806). There was no statistically significant difference in location of death between the two groups: acute care 20% EP vs 24% LEP, home 27% EP vs 13% LEP, hospice 36% EP vs 39% LEP, and tertiary palliative care unit 17% EP vs 24% LEP (p=0.251). Conclusions: EP and LEP patients with NSCLC had similar rates of CPHC service referrals and end of life planning. This suggests assistance of medical interpreters at the time of oncology visits help message delivery between LEP patients and HCPs. LEP patients had earlier referrals to CPHC prior to death which may reflect an enhanced awareness and effort by HCPs to have earlier conversations with patients who may have language and cultural barriers with discussing goals of care. Good communication improves patients and their family’s understanding of the goals and scope of palliative care services and allow HCPs to better understand the patients’ wishes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".