Bibliographic record
Abstract
Family nurse practitioners (FNPs) in British Columbia (BC) are trained in the care of young infants to elderly patients in all aspects of their healthcare. BC has three specialties available for NPs to practice in: family, pediatric, and adult. Currently, U.S. registered psychiatric NPs applying for registration in Canada/BC are not recognized as either a psychiatric NP or an NP due to regulations set by the College of Registered Nurses of British Columbia (CRNBC) and other provincial regulatory bodies. At the entry level of practice, BC NPs have full scope to care for a variety of mental illnesses including depression, anxiety, obsessive-compulsive disorder and substance use disorder, (CRNBC, 2015). Entry-level FNPs should acknowledge that psychiatric care is a large part of their practice and needs more attention than their current education may provide them. The best way to reduce the gap between psychiatric knowledge and physical health knowledge is interprofessional health care team collaboration (Hert et al., 2011; HFMH, 2006; McNeil, 2000; Thielke et al., 2007; Roberts et al., 2009). A professional poster was created with the intended purpose of providing entry-level NPs with a visual representation of some of the concerns experienced by family NPs, with some suggested strategies when providing health care to individuals with mental health issues.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.069 | 0.012 |
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".