Reconceptualizing the notion of victim selection, risk, and offender behavior in healthcare serial murders
Bibliographic record
Abstract
Purpose Beginning with the understanding that healthcare serial killers differ from traditional serial killers in terms of victim selection, risk and offender behavior, this paper attempts to reconceptualize how the motivations of healthcare serial killers are understood within the scope of care‐giving environments. Design/methodology/approach Drawing on the current literature surrounding serial homicide and serial killers, the paper argues that healthcare serial killers, by virtue of their profession, have an advantage in committing homicides that are less likely to be detected. Findings It is found that healthcare professionals work in an environment that is conducive to anti‐social behaviour like homicide. More specifically, recurring conditions within the work place (e.g. lack of a reporting system for problem employees, code of silence amongst employees) adds to the ease with which healthcare serial killers can evade capture. Originality/value Research examining healthcare professionals who kill their patients is limited. The current paper provisionally adds to the current understanding of serial homicide. While offering various explanations as to why healthcare serial killers are difficult to detect, this paper also explores some potential solutions for the monitoring of healthcare professionals and protecting the vulnerable patients in their care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".