Geslacht, gender identiteit en leeftijd: Invloeden op verschillen in de gezondheidszorg voor vrouwen met autisme in Nederland
Bibliographic record
Abstract
De afgelopen jaren is duidelijk geworden dat mensen met autisme onevenredig zwaar worden getroffen door ongelijkheiden op het gebied van gezondheid en gezondheidszorg (Krahn, Hammond en Turner, 2006; Emerson et al., 2011). Bestaande literatuur heeft aangetoond dat er verschillen bestaan tussen mannen en vrouwen met autisme met betrekking tot zowel hun diagnose, als hun toegang tot, duur en succes van therapieën. Wat ontbreektis onderzoek naar de mate waarin geslacht, gender, en dynamieken met betrekking tot de ontwikkeling van genderidentiteit van personen met autisme van invloed zijn op deze verschillen, en hoe laatstgenoemde variabelen kunnen samenhangen met andere dynamieken.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".