Bibliographic record
Abstract
This is lightly edited and referenced version of a presentation given at the 20th International Philosophy of Nursing conference in Quebec on 23rd August 2016. Philosophical texts are not given the same prominence in nurse education as their more valued younger sibling, primary research evidence, but they can influence practice through guidelines, codes and espoused values. John Stuart Mill's harm principle, found in On Liberty, is not a universal law, and only a thoroughgoing libertarian would defend it as such, though it, or its remnants, can be seen can be seen in policy documents. But its influence is weakening. Smoking bans in enclosed spaces were initially justified with other-regarding considerations, but judgements from unsuccessful legal challenges from patients in UK psychiatric hospitals rely on preventing harm to the smoker, even when smoking outside, which does not harm others. In the wake of legislation, no-smoking policies enacted by hospitals are becoming more aggressive, banning smoking both inside and outside, and extending the use of power gained through employment to prevent nurses assisting patients enjoy a lawful habit. Mill's dictum has been subverted, and this speaks to the fundamental purpose of nursing. Should nurses collude and willingly exert their power for their version of the good of the patient? Or should they instead reaffirm values that support and facilitate life choices made by autonomous people? The paper supports the latter option, and this has wider application for nursing which can be illuminated, if not settled, by revisiting Mill and his famous dictum.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.060 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".