Aplicación del Modelo Calgary de Evaluación Familiar en el contexto hospitalario y en la atención primaria a la salud. Revisión integradora
Bibliographic record
Abstract
Objective: Characterize articles published in online health journals concerning application of the Calgary Family Assessment Model (CFAM) in hospitals and in primary health care. Material and methods: An integrative review of the literature was conducted through a search of Capes, PubMed, Medline, Lilacs, BDENF, CidSaúde and IBECS. The following inclusion criteria were adopted: online articles available in full, published in Portuguese, English and Spanish from January 2008 to December 2013. The sample included of 25 works. Results: The largest output was in the years 2008, 2010 and 2013. Original articles predominated and 64.0% of the studies were carried out in the context of primary health care. As for authorship of the studies, the research showed that nurses participated in 88.0% of the publications. Conclusions: It was demonstrated that application of the CFAM, both in hospitals and in primary health care, allows for a more in-depth focus on the structure, development and functioning of the family, which is organized as a diagnosis of family identification.
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 | 0.042 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".