Greig Health Record for Young Adults: Preventive health care for young adults aged 18 to 24 years.
Bibliographic record
Abstract
OBJECTIVE: To describe the Greig Health Record for Young Adults (GHRYA), an evidence-based, peer-reviewed, endorsed guide that can assist providers with age-appropriate screening and counseling. SOURCES OF INFORMATION: A total of 521 articles were identified. Articles retained for review were those relevant to young adults and were population studies, guidelines, and systematic reviews. MAIN MESSAGE: Recently, there has been a recognition of the unique health care needs of the 18- to 24-year-old age group. Emerging adults have higher risks of health issues including mental illness, substance use, sexually transmitted infections, and risk-taking behaviour. Providing preventive care requires an age-specific approach, especially as contact with health care providers is often infrequent and episodic. Primary care providers who are less familiar with the preventive care needs of young adults can use the GHRYA to guide their interactions with these patients. This new tool is an easy-to-access guide to evidence-based recommendations to be used when patients present to the office or an urgent-care setting and a ready-to-hand place to record prevention strategies when delivered. The tool includes a checklist and 4 pages of resources and recommendations. CONCLUSION: The GHRYA is a peer-reviewed, endorsed guide to the provision of prevention and screening for young adults, which provides an approach to patient care but also evidence-based resources.
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 | 0.014 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.017 |
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".