Landscape of genetic counseling in the Philippines
Bibliographic record
Abstract
In this paper, we report on the professional development of genetic counselors in the Philippines as we discuss the status of genetic counseling training and research, along with the roles and scope of practice of genetic counselors. The development of a master's level training program for non-physician genetic counselors in the Philippines initiated in 2011 was in response to the increasing demand for genetic counseling services. There are currently 18 locally trained genetic counselors who are practicing in various fields including newborn screening, pediatrics, cancer, prenatal and preconception, neurology, and research. Despite the success of the genetic counseling training program, various professional challenges hinder maximizing the impact of genetic counselors in the health system. The challenges discussed in this paper include the limited number of genetic counselors, the lack of government positions officially recognizing the 'genetic counselor' title, and the absence of a regulatory framework. These issues require thorough discussion with appropriate government agencies and collaboration with other healthcare professional organizations with the ultimate goal of ensuring quality genetic counseling services nationwide.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".