Exploring Primary Care Physicians’ Experiences of Language and Cultural Discordant Care for Linguistic Minority Patients at the End-of-Life: A Study Protocol
Bibliographic record
Abstract
Health disparities exist across different linguistic groups. Language barriers in primary care can negatively affect access to healthcare services and the quality and safety of care at the end-of-life. This study will take a novel, in-depth look at the experience of language- and/or cultural-discordant care for adults from linguistic minority groups through the eyes of primary care physicians providing palliative and/or end-of-life care. Language and cultural discordance means that the physician and patient do not speak the same language or are not from the same cultural background. Qualitative data from primary care physicians (n = 12–24) providing language-discordant end-of-life care to Francophone and/or Allophone older adults across different care models and diverse geographies in Ontario will be collected through semi-structured interviews. Reflexive thematic analysis will be used to report themes within the data and consider the influence of the social locations of the researcher and research participants, geographic considerations impacting service provision, and barriers imposed by differing primary care funding structures on the provision of palliative and end-of-life care for linguistic and cultural minority groups in Ontario. Findings from this study will identify the interconnections among language and cultural discordance, care model, geographic region, and physician perceptions of their combined effects on access to, and quality of, palliative and end-of-life care. This evidence will be key to informing clinical practice guidelines and mobilizing change to improve primary care access and quality for adults at the end-of-life from linguistic and cultural minority populations across Ontario.
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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 | 0.054 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".