Identifying opportunities for collaboration and growth of genetic counseling services in the Asia Region
Bibliographic record
Abstract
The Genetic Counseling Pre-Conference Workshop (GCPCW) was held on September 16, 2015, in Hanoi, Vietnam. We report the GCPCW outcomes obtained from pre- and post-conference questionnaires, case-review breakout session, and an open discussion of needs for genetic counseling services in the Asia region. The GCPCW participants completed questionnaires with closed- and open-ended questions regarding the status and needs of providing genetic counseling services in Asia. Utilizing thematic content analysis, common themes shared during the case-review breakout session are summarized and survey results are tabulated. Of the 71 participants, pre- and post-conference questionnaires were returned by 57 (80%) and 44 (62%) individuals, respectively. Of the 42 participants who did not identify themselves as students in training, 36 (86%) stated they are currently providing genetic counseling services. Participants cited that the most useful information obtained during the GCPCW related to the status of genetic counseling services in the region, discovery of shared challenges, professional networking, and the need to establish genetic counseling training programs and its accreditation. The GCPCW provided a collaborative forum to address current challenges and needs of genetic counseling services in the region. Strategies were identified to foster genetic counseling training and clinical service opportunities.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".