Fondamentaux des soins : un cadre et un processus pratique pour répondre aux besoins physiques, psychosociaux et relationnels des personnes soignées
Bibliographic record
Abstract
Since 2008, an international group has been helping to promote a better response to the fundamental needs of individuals receiving care. This group provides a framework on the fundamentals of care that focuses on the relationship between the nurse, the individual being cared for, and his or her relatives, as well as on the response to the patient’s physical, psychosocial, and relational needs. A practice process supports the concrete application of this framework. The purpose of this discursive article is to present the French translation of the Fundamentals of Care Framework and its Practice Process. To begin with, the translation process will be briefly explained. Next, the Fundamentals of Care Framework and the stages in its Practice Process will be presented. To help the reader better understand the proposal, a clinical illustration will be used to present the situation of Mr. Perron, who is living with Alzheimer’s disease, and his spouse, who is his family caregiver. Finally, the article discusses the usefulness of the Fundamentals of Care Framework and its Practice Process in terms of the four main areas of the discipline of nursing : practice, management, training, and research. This article paves the way for the development of knowledge on the fundamentals of care in the French-speaking world.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".