Health-literate healthcare organisations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There has been a rapid increase in the number of publications on health literacy in general, but also specifically on organisational health literacy, health-literate healthcare organisations (HLHCOs) or health-literate organisations (HLOs). The discourse on HLOs, like the one on health literacy, started in the US, but has increasingly been taken up, adapted and further developed in other countries such as Australia, Austria, Belgium, Canada, Germany, Italy, Israel, Norway, Taiwan and New Zealand, and there are already several literature reviews or overview articles on organisational health literacy (Palumbo, 2016; Brach, 2017; Meggetto et al, 2017; Farmanova et al, 2018; Lloyd et al, 2018) that support orientation about this rapidly evolving field of research, practice and policy. While from its beginning health literacy was introduced as a measurable and modifiable concept, based on the long tradition of measuring and teaching literacy, instruments for HLO measurement and modification are still being developed. Measurement of the functional health literacy of patients had already begun in the US in the 1990s, and produced empirical evidence that health literacy matters for healthcare: first, a considerable number of patients have low or limited (functional) health literacy, and this proportion is likely to increase (Parker et al, 2008). Second, patients with low (functional) health literacy have higher use and worse outcomes of healthcare services (Berkman et al, 2011; Brach et al, 2012). And third, low health literacy in healthcare also has considerable consequences for the costs of health care (Eichler et al, 2009). These facts, taken up by the former Institute of Medicine and supported by the health policy of the US government, led to a focus by practitioners and researchers on the limited health literacy of patients within the healthcare system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it