Bibliographic record
Abstract
Health care services increasingly face patient populations with high levels of ethnic and cultural diversity. Cultures are associated with distinctive ways of life; concepts of personhood; value systems; and visions of the good that affect illness experience, help seeking, and clinical decision-making. Cultural differences may impede access to health care, accurate diagnosis, and effective treatment. The clinical encounter, therefore, must recognize relevant cultural differences, negotiate common ground in terms of problem definition and potential solutions, accommodate differences that are associated with good clinical outcomes, and manage irresolvable differences. Clinical attention to and respect for cultural difference (a) can provide experiences of recognition that increase trust in and commitment to the institutions of the larger society, (b) can help sustain a cultural community through recognition of its distinct language, knowledge, values, and healing practices, and (c) to the extent that it is institutionalized, can contribute to building a pluralistic civil society.
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 | 0.031 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.107 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".