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.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.020 |
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".