A Model for Promote and Strengthen Individual/ Family Health: McGill Nursing Model
Bibliographic record
Abstract
With advances in science, the nursing profession has become mandatory to create their own unique the content of scientifi c knowledge. In this context, the nursing leaderships emphasized the importance of development models and theories to contribute the profession. Theories originally used as a guide to create the conceptual framework of nursing education, later these theories infl uenced nursing practice. The concept of individual and family strengths as a central concept of the McGill Model of Nursing is one of these models. The McGill Model of Nursing was developed under the guidance of Dr. Moyra Allen and Mona Kravitz in McGill Nursing Faculty in the 1970s and implemented in various practice settings in Canada. When fi rst created, previously named as the “Situation-responsive Nursing”, “Allens’ Nursing Model”, “Complemental Nursing, “Developmental Model of Health & Nursing”. The McGill Model of Nursing’s main objective is improve, strengthen and maintain of individual’s and family’s health. In this model, the importance of nursing roles in improving, supporting and completing individual and family capacity to present conditions, diseases, disabilities and other illnesses to tackle the challenges which may arise and adopt in the “natural healing process” were emphasized. The purpose of this review to explain the conceptual framework of the McGill Model of Nursing and the use of the model to guide nursing practice in our country.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".