Ansätze zur Verbesserung der hausärztlichen Versorgung von Menschen mit Migrationshintergrund mit Fokus auf die Demenzdiagnostik
Bibliographic record
Abstract
People with a migration background represent more than a quarter of the German population and are increasingly at risk of suffering from dementia due to demographic change. Diagnosing dementia depends on language skills, cultural backgrounds, knowledge and access of patients to the healthcare system. Although general practitioners (GPs) hold a key role in diagnosing dementia in Germany, it is unknown whether they face challenges and are in need of support to interact with patients with a migration background. In addition, the access of people with a migration background to GP services is unclear. This thesis aims to address these gaps in research. A cross-sectional survey in a random sample of 339 GPs in North Rhine Westphalia (NRW) was conducted from October 2017 to January 2018 (response rate: 34.5 %). A self-developed, standardized questionnaire was used to gather GPs’ experience in diagnosing dementia and analysed performing descriptive and multiple logistic regression analyses. The connection of a migration background and further factors and having no GP was analysed among the 7755 participants of the representative “German Health Interview and Examination Survey for Adults”. Descriptive analyses and multiple logistic regression models were conducted. A share of 96 % of GPs reported having experienced barriers in diagnosing dementia in their patients with a migration background at least once. Uncertainties in this field were stated by 70.9 % with no significant association to GPs’ sociodemographic characteristics. Language barriers (89.3 %), information deficits (59.2 %) and shameful interaction or lack of acceptance of the syndrome (55.5 %) on the part of patients were reported most frequently. A demand for information on the topic was expressed by 70.6 % of GPs. In DEGS1, an increased share of 14.8 % of people with a migration background had no GP, especially those with a two-sided background (aOR: 1.90, 95 % CI: 1.42–2.55). To prevent unequal health opportunities, GPs should be supported in providing healthcare and especially in performing dementia diagnostics in their patients with a migration background. Intercultural opening of the healthcare system through language and culturally sensitive information, intercultural competence training of GPs and a focus in politics and research could be useful to improve healthcare.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".