Bibliographic record
Abstract
What is to be learned from this situation? First, although Mr. Lockwood's consent for the DNR order is not needed, there is an obligation to communicate openly and clearly with the family and ensure that Mrs. Lockwood's advance directive is respected. This might mean a DNR order needs to be written. Also, there is an obligation to discuss goals of care with the family. The second lesson is that you should reflect on your employer's CPR policies and practice, and ask the following questions: Do the policy and/or practices support saying "no" in a situation such as Mrs. Lockwood's? Also, how does the policy support staff when there is a request for futile CPR, either from a competent patient or from a patient's family? What are the expectations about communication with the family when there is an advance directive and/or when CPR is found to be futile? Knowing what you ought to do for patients is not sufficient. Often you cannot act on these decisions because of the environment. If the policies are not in accord with the CNA Statement on Resuscitative Interventions, you should collaborate with colleagues to revise the CPR policy and practices. By doing so, you will be meeting your obligation to help foster and support a practice environment that promotes ethical, competent and compassionate nursing care.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".