Integrating Health Literacy Policy into Health Reform in Austria (within the context of a European perspective)
Bibliographic record
Abstract
Within the European context Austrian health policy rather early got interested in measuring and improving population health literacy (HL) and systematically invested in integrating HL in its health reforms in the last decade. Therefore within a HEN report on policies of HL (Rowlands et al 2018) Austrian HL policy is well represented and Austria is the only European county included in a recent article on national policies and strategies for HL (Trezona, Rowlands and Nutbeam, 2018). Based on the early experiences in measuring population HL in the US, Canada and Australia, but also in Switzerland, Austria became interested in measuring HL and was active in initiating the first European comparative study on population HL (HLS-EU). It did not only take part in this study but was responsible for the work package on analyzing and reporting results of this study and undertook follow up studies on HL in Austrian regions, HL of adolescent, HL of selected migrant groups and on organizational HL of hospitals. Comparative results of the HLS-EU study were published at the time when Austrian health targets were in discussion and these results showed that HL in Austria was rather limited compared to the other participating European countries. Therefore HL got high attention in the process of defining and deciding of all in all 10 health targets with target no. 3 on ＂Improving the Health Literacy of the Population＂. Health targets and further measures were developed in a participatory transparent process involving many relevant stakeholders and citizens. Further on the HL health target was prioritized and a catalogue of measures was developed. Aspects relating to the healthcare field are being implemented through the ongoing healthcare reform process, while aspects relating to the 'health in all policies' dimensions of HL are being implemented through the newly established intersectoral Austrian Health Literacy Platform. This platform among other activities organizes national annual HL conferences (with about 300 participants on average), offers a web site and a regular newsletter. From early on, among the chosen topics the relational character of the HL concept has been considered and measures for improving organizational HL respectively health literate organizations have been supported. Due to the wish of the Austrian government to have regular comparative surveys in Europe for monitoring and benchmarking population and organizational HL, Austria together with Germany, Switzerland, Luxemburg and Liechtenstein engaged in initiating an Action Network Measuring Population and Organizational Health Literacy (M-POHL) within the European Health Information Initiative (EHII) of WHO-Europe. Austria is chairing M-POHL in its initial phase. At M-POHLs kick-off meeting in Vienna The Vienna Statement on the measurement of population and organizational health literacy in Europe＂ was launched. About 20 counties from the WHO-Europe region are already involved in the action network; a first HL population survey is prepared for 2019. First Conclusions: To get public and political attention population health literacy has to be measured comparatively and results have to be reported and discussed publicly. Measures for improving health literacy have to be integrated into ongoing general health reforms and policy, but specific institutions supporting continuous development of HL have to be created, installed and supported. To be successful many different stakeholders have to be recruited and coordinated. International monitoring of HL can support national policies.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".