Open Dialogue: Frequently Asked Questions
Bibliographic record
Abstract
Open Dialogue is an approach to working with people and their families experiencing psychosocial distress. Interest in Open Dialogue in Australia has been growing recently, raising questions about its adaption and implementation to local contexts. This article is an attempt to answer some of the frequently asked questions we have encountered in training and discussions about Open Dialogue. We attempt to provide responses to questions of how Open Dialogue is different to what is done already, how it fits with current approaches, how you know if you are doing it, whether it is passive or just about doing reflections, issues about including the social network, and concerns about the evidence base. This article aims to present a variety of viewpoints in relation to these questions and to hopefully further discussions on how Open Dialogue can be implemented and adapted to Australian health care and social care 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 | 0.210 | 0.357 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".