THE CNA CODE OF ETHICS PART II: NURSES WORKING TOWARD ENDING SOCIAL INEQUITIES
Bibliographic record
Abstract
Nurses as a group have the knowledge and possess the compassion that is needed to improve the lives of the underprivileged. As mandated by the (CNA) Code of Ethics Part II, thirteen specific ways to eliminate social inequities are presented as a means of addressing and/or eliminating social inequities. Poverty is a worldwide problem that has been growing instead of subsiding and has a negative impact on people’s health. Because socioeconomic determinants of health are closely tied to health outcomes, the poor use additional and more expensive health services. In order to positively impact populations that are suffering, one effective way to get the attention of policy makers is to build a business case that demonstrates cost saving measures. Homelessness is on the increase in Canada. The perception is that it is too expensive to provide housing and treatment for people who suffer from addictions and/or mental illness is not necessarily supported by fact. A review of a recent study commissioned by government provides evidence that a substantial amount of money could be saved if housing and treatment was made available for these people. Ideas are suggested that may help nurses to instigate other positive changes in the lives of others, not just for some people but for everyone.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".