Bibliographic record
Abstract
This book examines existing research into communication between health care providers and minority ethnic1 health care users who lack fluency2 in English. The book should be of value to academics, researchers, and students in health care and also the sociology of health, and to health service practitioners and leaders. It takes account of research conducted within health care disciplines, while complementing this with perspectives derived from the sociology of health and communication. The strong dual focus on empirical research and on communication sets it apart from other books focused on minority ethnic users and health, which have tended to have a less empirical flavour (Robinson, 1998), or to focus more generally on ethnicity (Ahmad, 1993). The book does not exhaustively explore the full range of barriers to health care facing minority ethnic patients. Many members of minority ethnic groups in the UK do not lack fluency in English; an increasing number are born and raised here. Yet many of the communication barriers they face doubtless overlap with those of non-fluent speakers, for example concerning institutional and attitudinal rather than strictly linguistic factors; however, not all the research touching on their needs is covered here. At the same time, the scope of the book remains wide-ranging, and it includes research conducted in several countries where English is a national language, widely used in health care, particularly the US, Canada, and Australia, as well as the UK. As a result, a considerable diversity of minority ethnic groups and health care contexts is considered. Some facets of the research are rather context-dependent, indeed the robustness of communication research may require context-sensitivity, yet many aspects of each study should have broad application wherever minority ethnic users not fluent in English strive to have their communication needs understood and met.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.567 | 0.438 |
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".