O diálogo aberto e os desafios para sua implementação – análise a partir da revisão da literatura
Bibliographic record
Abstract
METHOD: The Open Dialogue Method was developed in Finland in order to deal with severe psychotic crises using dialogue and social network inclusion. By means of a review of the literature on the Open Dialogue Method, this article sought to identify the principles and contributions for deinstitutionalization.The PubMed (365), PsycInfo (134), Lilacs (no articles found) databases and 2 books were consulted. Thirty-four publications that fulfilled the requirements of this review were selected. The search was conducted in October 2015. The key words used were: open dialogue, crisis, first psychotic episode, schizophrenia, family therapy, need-adapted approach. RESULTS: There were 3 reviews, 5 theoretical studies, 21 qualitative studies and 5 quantitative studies. Two of them were written in Italian, one in French and thirty-one in English. The authors were from Norway, the United States, Finland, Australia, the United Kingdom, Belgium, Canada and Poland. The publications were grouped for purposes of analysis into the following categories: Open Dialogue concepts and principles; Open Dialogue contributions; Challenges for Open Dialogue implementation in other countries, realities and contexts.
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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 | 0.028 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".