A Role for Health Literacy in Protecting People With Limited English Proficiency Against Falling: A Retrospective, Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors related to falls within the scope of speech-language pathology (SLP) using assessments from the Inpatient Rehabilitation Facility-Patient Assessment Instrument over a 4-month period in 4 inpatient rehabilitation facilities (IRFs). DESIGN: Observational retrospective cohort study. SETTING: Four IRFs as part of a larger learning health system. PARTICIPANTS: Adults aged ≥18 years admitted to the IRFs from October 1, 2022 to February 28, 2023 were included. INTERVENTION: N/A. MAIN OUTCOME MEASURES: Occurrence of falls. RESULTS: Analyses of 631 patient records revealed that the odds of falling were almost 3 times greater in people with limited English proficiency than in English speakers (odds ratio [OR], 2.92; 95% confidence interval [CI], 1.09-6.85). People with limited English proficiency who reported poorer health literacy had 4 times higher odds of falling (OR, 3.90; 95% CI, 1.13-13.44) than English speakers who reported adequate health literacy. People with limited English proficiency who reported adequate health literacy had the same risk of falling as English speakers (OR, 0.98; 95% CI, 0.16-6.12), suggesting the protective role of health literacy for people with limited English proficiency. CONCLUSIONS: Language barriers have a significant effect on falls among patients in IRFs. SLPs improving health literacy and providing language support may play a crucial role in mitigating fall risk, thereby enhancing patient safety and outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".