Bibliographic record
Abstract
Terrorism is becoming more prevalent in the world today. We see this through mass shootings, suicide bombers and vehicular explosions. Healthcare profession will be called upon to provide care to all people involved in these acts, including the perpetrator. Many health care professionals are likely ethically and emotionally unprepared for providing health care to terrorists. It is important to consider the potential ethical dilemma that may arise; whether the healthcare provider is obligated to care for a terrorist like any other patient that may be in their care. It is important to consider that regardless of personal values and beliefs of the healthcare professional and of society, terrorists are still entitled to medical care through the International Humanitarian Law. Like any other criminal, murderer or rapist who inflicts harm, terrorists are still protected by this law, and obligated to receive treatment in an ethical and humane way. In hopes of uncovering the underlying issue of terrorists rights to medical treatment and how it impacts nursing care, this poster will help guide future recommendations and research for those healthcare providers caring for terrorists. Discipline: Nursing Faculty Mentor: Lisa McKendrick-Calder
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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".