Bibliographic record
Abstract
In early 2005, NENA conducted a poll of its members regarding the potential reporting of gunshot wounds (GSW) to police by emergency department staff.Since several provincial governments were considering possible legislation, NENA sought timely input from NENA members on this important and controversial issue.Twenty-one per cent of NENA members took advantage of the opportunity to participate in this poll (an excellent percentage poll response).Of the NENA members who responded, 96.5% were in support of mandatory GSW reporting, and only 3.5% were opposed.After formulating the member responses, the NENA Board of Directors sent a letter to all provincial and federal health ministers as well as to all health care stakeholders across Canada.An excerpt from that letter follows:"The majority of NENA poll respondents believe that we are not only responsible for our individual patients, but that, as emergency nurses, we also have a responsibility to all other patients, visitors, colleagues, our communities and society as a whole.It should be recognized that the role of the emergency nurse would be solely to inform law enforcement agencies.The conclusion of the NENA poll indicates that public safety must be the priority in this serious issue."The NENA board of directors thanks the NENA membership for their participation in this NENA poll.Watch future issues of Outlook for other membership polls.With active member participation, NENA can truly be the voice of Canada's emergency nurses.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.340 | 0.169 |
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".