Reading and writing reviews: A primer on systematic, scoping, and narrative reviews for genetic counselors
Bibliographic record
Abstract
For genetic counselors, critiquing and using published literature is crucial to staying at the top of practice, and the ability to critique and use review articles is no exception. Understanding distinct types of reviews and the questions they can answer is therefore an important skill for genetic counselors across practice specialties, professional roles, and experience levels. Additionally, knowing how distinct types of reviews are formally developed and written unlocks opportunities beyond traditional original research studies for genetic counselors to engage as authors in the rigorous academic work and publications needed in our profession. This article aims to help genetic counselors develop a functional understanding of three review types: systematic, scoping, and narrative. Considerations for interpreting and writing these types of reviews are provided, with resources that may serve as a starting point for those interested in going deeper.
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 | 0.164 | 0.267 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".