Bibliographic record
Abstract
Summary of the thesis: Nurse training was born in the 1870's spurred on by doctors from the Red Cross and in the state-owned hospitals of Paris. Leonie Chaptal played a fundamental role in the elaboration of the first curriculum, a curriculum based on the knowledge which is useful for the nurse to assist the doctor. However, school based in France from Florence Nightingale's trend emphasize the professional autonomy of the nurse. Nursing training and profession therefore evolve in an ambivalence which leads to develop either "caring techniques" close to medical techniques, or a financial autonomy claimed by the "appropriate role" and the nursing clinical approach. Today the nursing profession finds itself in the heart of reforms: transfer of competence, validation of the acquired knowledge from experience, reform of the curriculum with a possible connection with the university. Having defined the criteria of the science by taking example on the model of the sciences of education, the study of nursing research published in the ARSI from 1985 in 2005 shows that nursing research exits and gives a general idea of investigated subjects, often referred to human sciences; hence a first approach of reference sciences, on which nursing sciences can establish and develop, knowing that the title nursing sciences is only used in Quebec.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".