Bibliographic record
Abstract
A registered nurse of questionable professional competence, with a dodgy employment record, a history of mental illness and substance abuse, remains employed and ultimately murders eight vulnerable seniors, attempts to murder four others, and assaults another two over the course of a decade.At least those are the ones for which Elizabeth Wettlaufer offered a confession.Like most nurses and citizens, I was horrified by the revelation of multiple homicides at the hand of one of us.How could such a confluence of incompetence, mental illness and addiction, and willful murder go undetected for so long by so many?Notwithstanding that she is serving eight concurrent life sentences, Elizabeth Wettlaufer provided her own insights as to what system changes would have limited her capacity to kill: a) better controls on drugs such as insulin, b) better visibility and monitoring of activities within medication rooms, c) active mental health follow-up for persons/health care givers like herself, and d) provision of advocates for seniors with dementia (Dubinski 2018).Albeit all reasonable and actionable suggestions from the mind of a serial killer but let's be honest, a much higher bar of accountability, actions and preventive measures are being expected from our profession.
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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".