Prenatal genetic counseling in cross-cultural medicine: A framework for family physicians.
Bibliographic record
Abstract
OBJECTIVE: To help family physicians practise effective genetic counseling and offer practical strategies for cross-cultural communication in the context of prenatal genetic counseling. SOURCES OF INFORMATION: PubMed and the Cochrane Database of Systematic Reviews were searched. Most evidence was level II and some was level III. MAIN MESSAGE: The values and beliefs of practitioners, no less than those of patients, are shaped by culture. In promoting a patient's best interest, the assumptions of both the patient and the provider must be held up for examination and discussed in the attempt to arrive at a consensus. Through the explicit discussion and formation of trust, the health professionals, patients, and family members who are involved can develop a shared understanding of appropriate therapeutic goals and methods. CONCLUSION: Reflecting on the cultural nature of biomedicine's ideas about risk, disability, and normality helps us to realize that there are many valid interpretations of what is in a patient's best interest. Self-reflection helps to ensure that respectful communication with the specific family and patient is the basis for health care decisions. Overall, this helps to improve the quality of care.
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 | 0.076 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".