The impact of patient-facility language discordance on potentially inappropriate prescribing of antipsychotics in long-term care home in Ontario, Canada: a retrospective population health cohort study
Bibliographic record
Abstract
BACKGROUND: Appropriate use of medication is a key indicator of the quality of care provided in long-term care (LTC). The objective of this study was to determine whether resident-facility language concordance/discordance is associated with the odds of potentially inappropriate prescribing of antipsychotics (PIP-AP) in LTC. METHODS: We conducted a population-based, retrospective cohort study of LTC residents in Ontario, Canada from 2010 to 2019. We obtained resident language from standardized resident assessments, and derived facility language by determining the proportion of residents belonging to each linguistic group within individual LTC homes. Using linked administrative databases, we identified all instances of PIP-AP during a 1-year follow-up period. PIP-AP was defined using the STOPP-START criteria, which have previously been shown to predict adverse clinical events such as emergency department (ED) visits and hospitalizations. The association between linguistic factors and PIP-AP was assessed using adjusted multivariable logistic regression analysis. RESULTS: We identified 198,729 LTC residents consisting of 162,814 Anglophones (81.9%), 6,230 Francophones (3.1%), and 29,685 Allophones (14.9%). The odds of PIP-AP of were higher for both Francophones (aOR 1.15, 95% CI 1.08-1.23) and Allophones (aOR 1.11, 95% CI 1.08-1.15) when compared to Anglophones. When compared to English LTC homes, French LTC homes had greater odds of PIP-AP (aOR 1.12, 95% CI 1.05-1.20), while Allophone homes had lower odds of PIP-AP (aOR 0.82, 95% CI 0.77-0.86). Residents living in language-discordant LTC homes had higher odds of PIP-AP when compared to LTC residents living in language-concordant LTC homes (aOR 1.07, 95% CI 1.04-1.10). CONCLUSION: This study identified linguistic factors related to the odds of PIP-AP in LTC, suggesting that the linguistic environment may have an impact on the quality of care provided to residents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".