Análise de conceito de protocolo em saúde à luz do método evolucionário de Rodgers
Bibliographic record
Abstract
Objective: to analyze the concept of protocol in health using Rodgers' evolutionary method. Method: this is a conceptual analysis based on Rodgers' evolutionary model. Data collection was carried out in December 2021 in the following databases: CAPES Theses and Dissertations Portal, DART-Europe E-Theses Portal, Electronic Theses Online Service (EThOS), Repositório Científico de Acesso Aberto de Portugal (RCAAP), and Theses Canada. The DeCS/MeSH descriptors “Protocolo/Protocol” and “Saúde/Health” were used. To analyze the studies, the year of publication, country of origin, concept, attributes, antecedents, consequents, substitute terms, and related concepts were evaluated. Results: in the antecedents and consequents, determining terms were identified for the construction of the concept related to health care, protection of professionals, and improvement of patient care. The attributes of the concept included academic and quality knowledge for patient care. Conclusion: the concept analyzed is comprehensive, involving substitute terms and concepts related to the practice and quality of health care, from which the definition of the concept of health protocol was developed.
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 | 0.089 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".