need for alternative solutions when caring for patients with language barriers
Bibliographic record
Abstract
Barriers to quality communication increase the risk for misunderstanding, negatively impact the thoroughness of health investigations, and can lead to delayed diagnoses and increased readmissions. In addition, language barriers disproportionately affect the most vulnerable populations; thus, a lack of appropriate interpretation services promotes health disparities and increases the vulnerability of the underserved minority populations. According to the Act Respecting Health Services and Social Services of Quebec, health organizations need to take into account the distinctive linguistic and sociocultural characteristics of each region and, “foster […] access to health services and social services through adapted means of communication for persons with functional limitations”. A language barrier is a form of functional limitation that patients face when accessing healthcare services. Despite a clear policy, the current use of professional interpretation services is limited in our healthcare facilities, thus increasing obstacles in accessing healthcare services for patients with language barriers. It is thought that by identifying how language barriers present in our healthcare system and by highlighting the tools available to mitigate their consequences, healthcare workers, including medical students, may be better placed to serve the non-French and non-English speaking community. A group of medical students from the Universities of Montreal and McGill who are part of MedComm researched the problematic, most specifically in Montreal, in the hopes of emphasizing the need for alternative solutions to the current state of affairs in regard to offering optimal care to patients with language barriers.
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
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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".