Avaliação familiar: do Modelo Calgary de avaliação da família aos focos da prática de enfermagem
Bibliographic record
Abstract
Family nursing is a specific area in the general context of nursing, emphasizing the interactions of the elements of the family, in a systemic perspective of care. Nursing intervention requires the use of models enabling the design of care geared to the collection of data and the planning of interventions. Therefore the Calgary Family Assessment Model (CFAM) is useful to understand the family in its multidimensionality. The study aimed to identify areas of nursing care in the family health context. Using the methodology of action research, the sample was formed by nurses of a Health Centre in Northern Portugal, using the technique of group discussion. From the results emerged a reconstruction of the Care Model through the combination of the focus of the practice described in the International Classification for Nursing Practice with the concepts of CFAM. In this model co-built evaluative categories have been specified to define the areas of attention in the context of Family Nursing. We believe to be a first step in the definition of the knowledge in this specific area, as the aspects that influence health states, which determine the health nursing care, concern the knowledge of nursing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".