Regulating open disclosure: a German perspective
Bibliographic record
Abstract
The issue of open disclosure has received growing attention from policy-makers, legal experts and academic researchers, predominantly in a number of English-speaking countries. While implementing open disclosure in practice is still an on-going process, open disclosure now forms an integral part of health policy in various American states, the UK, Canada, Australia and New Zealand, with a number of measures having been put in place to encourage open disclosure and to mitigate some of the barriers to such open communication. In contrast, this issue has received little attention in non-English-speaking countries and there is currently no empirical data relating to actual practice or practitioners' attitudes and views in most countries in continental Europe. This article critically examines Germany's current approach to open disclosure. It finds that the issue plays no significant role in German health policy with very limited measures explicitly concerning such communication currently in place. While a number of aspects of the wider regulatory framework appear to be supportive, Germany is still in the early stages of a systematic approach and additional measures are required to further promote open disclosure within the self-governing German healthcare system. This exploration provides an example of a non-English-speaking country's approach to open disclosure and may be of particular interest to neighbouring German-speaking and civil law countries such as Switzerland and Austria.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.014 | 0.007 |
| 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".