L’infermiere come agente morale della professione, la sensibilita etica e i valori professionali
Bibliographic record
Abstract
The purpose of this thesis is aimed to answer to the question: nurse's ethical sensitivity today is consistent with the definition of nurse as moral agent of the profession? In order to answer, nurses will be defined as moral agent of the profession through the revision of most representive ethical currents in bioethics (Principialism, ethics of virtues, ethics of natural law and ethics of respect for people) motivating so his ethical/deontological involvement within the intellectual professions and the health system; we will proceed to identify which values underlie the profession and form the foundation for the professional moral agent, starting from those explained in the UN declaration in 1948 and researching them in the most representative codes of ethics, Italy, Canada, Australia, New Zealand, International Council of Nurses. The concepts that define the professionals, caring, advocacy, accountability and cooperation, will be a useful basis, together with the compassion defined as a qualifying component, to help to define the moral agent. Then the Nurses Professional Values Scale-R (NPVS) questionnaire, of the American colleagues Darlene Weis and Mary Jane Schank, will be administered to a representative sample of professionals in order to identify the ethical sensitivity of the same today; in the conclusions will be compared the data collected through the questionnaire with the definition of nurse as moral agent of the profession.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".