Political action in nursing and medical codes of ethics
Bibliographic record
Abstract
Political action has a long history in the health workforce. There are multiple historical examples, from civil disobedience to marches and even sabotage that can be attributed to health workers. Such actions remain a feature of the healthcare community to this day; their status with professional and regulatory bodies is far less clear, however. This has created uncertainty for those undertaking such action, particularly those who are engaged in what could be termed 'contentious' forms of action. This study explored how advocacy and activism were presented in nursing and medical codes of ethics, comparing disciplinary and temporo-spatial differences to understand how such action may be promoted or constrained by codes. The data for this study comes from 217 codes of ethics. Because of the size of the corpus and to facilitate analysis, natural language processing was utilised, which allowed for an automated exploration of the data and for comparisons to be drawn between groups. This was complemented by a manual search and contextualisation of the data. While there were noticeable differences between medical and nursing codes, overall, advocacy, activism and even politics were rarely discussed explicitly in most codes. When such action was spoken about, this was often vague and imprecise with codes speaking of 'political action' and 'advocacy' in general terms. While some codes were far more forthright in what they meant about advocacy or broader political action (i.e., Nursing codes in Denmark, Norway, Canada) more forceful language that spoke in specific terms or in terms of oppositional or specific actions (e.g., civil disobedience or marches) was almost completely avoided. These results are discussed in relation to the broader literature on codes and the normative questions they raise, namely whether such action should be included in codes of ethics at all.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.010 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".