Which factors influenced the adoption of interprofessionality in health based on the reports of the PET-Health Interprofessionality projects in Brazil? A document analysis
Bibliographic record
Abstract
The Program of Education through Work - Health (PET-Health) Interprofessionality is one of the strategic actions of the "Plan for the Strengthening of Interprofessionality" in healthcare in Brazil. Based on the experience of the program, this paperexamines the aspects that impact the adoption and strengthening of interprofessional education and collaborative practices, and issues recommendations for the strengthening of interprofessionality as a guiding principle of training and working in healthcare. This is a document analysis of partial reports from the six- and 12-months of execution of 120 PET-Health Interprofessionality projects in Brazil. The data were analyzed based on content analysis and the categories elaborated a priori. The aspects that impact the adoption and strengthening of interprofessionality in training and working in healthcare, and future recommendations, were organized in the relational, processual, organizational, and contextual dimensions, according to the framework by Reeves et al. The PET-Health Interprofessionality expanded the understanding of elements of interprofessional education and practice and showed that the discussion must take on a more political, critical, and reflexive character. The analysis points to the need for continuity of teaching-learning activities as a strategy to foster interprofessional capacity in healthcare services and consequent strengthening of the Unified Healthcare System in Brazil.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".