Bibliographic record
Abstract
Serendipity leads forensic nursing to the Yukon Sheila EarlyAccording to Wikipedia, serendipity refers to "the accident of finding something good or useful while not specifically searching for it" or "a happy surprise".A chance meeting with Deborah Crosby at the 2011 International Association of Forensic Nurses (IAFN) Scientific Assembly in Montreal led to such a happening.Deb is the nurse in charge of a primary health care centre in Carmacks, YT, serving a population of more than 500 people.She is a member of both NENA and IAFN.She has also worked as a sexual assault nurse examiner in Ontario.She approached me after attending a session on nurse examiner programs, which I co-presented.She was interested in the most current practices in the forensic nurse examiner role, as she often is called on to perform examinations in her centre.She asked if it would ever be possible to have a nurse examiner course delivered in the Yukon.We both went on our way.However, I was impressed with her enthusiasm and desire to improve the services provided to those who have sexual violence in their lives.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".