Kennisvraag: Wat kan Nederland leren van andere landen op het gebied van patiëntvertegenwoordiging,\ninformatievoorziening en lotgenotencontact?
Bibliographic record
Abstract
In vergelijking met andere landen heeft Nederland een sterk ontwikkelde patiëntenbeweging met veel aandacht voor belangenbehartiging, informatievoorziening en lotgenotencontact. Tegelijkertijd is er veel belangstelling in Nederland voor manieren om de positie van de patiënt verder te versterken. In dit licht is onderzocht wat Nederland mogelijk van andere landen zou kunnen leren. Op grond van een QuickScan van 10 landen zijn 4 landen gekozen voor verdiepend onderzoek naar veelbelovende vormen van belangenbehartiging, informatievoorziening en lotgenotencontact. Dit waren Duitsland, Canada, de Verenigde Staten en het Verenigd Koninkrijk. Het verdiepende onderzoek is gedaan middels interviews met experts en op grond van beschikbare publicaties en online bronnen.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".