Medical Genetic Counseling for Breast Cancer in Primary Care: A Synthesis of Major Determinants of Physicians' Practices in Primary Care Settings
Bibliographic record
Abstract
OBJECTIVES: This paper aims to identify relevant potential predictors of medical genetic counseling for breast cancer (MGC-BC) in primary care and to develop a comprehensive questionnaire to study MGC-BC. METHODS: A scoping review was conducted to identify the predictors of MGC-BC among primary care physicians. Relevant articles were identified in selected databases (PubMed, Embase, CINAHL, ISI Web of Science, PsycINFO, and Cochrane CENTRAL) and 4 selected relevant electronic journals. RESULTS: An inductive analysis of the 193 quantitatively tested variables, conducted by 3 researchers, showed that 6 conceptual categories of determinants, namely (1) demographic, (2) organizational, (3) experiential, (4) professional, (5) psychological, and (6) cognitive, influence MGC-BC practices. CONCLUSION: There is a scarcity of literature addressing the medical behavior determinants of MGC-BC. Future research is needed to identify effective strategies put into action to support the integration of MGC-BC in primary care medical practices and routines. However, our results shed light on 2 levels of actions that could improve genetic counseling services in primary care: (1) medical training and educational efforts emphasizing family history collection (individual level), and (2) clarification of roles and responsibilities in ordering and referral practices in genetic counseling and genetic testing for better healthcare management (organizational level).
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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".