Defining, assessing, and implementing organizational health literacy: barriers, facilitators, and tools – a systematic review
Bibliographic record
Abstract
BACKGROUND: Organizational health literacy (OHL) is increasingly recognized as a fundamental aspect of high-quality healthcare delivery, focusing on organizations' roles in enabling patients to access, understand, and use health information effectively. This systematic review synthesizes current research on OHL, focusing on its definitions, assessment tools, implemented practices, outcomes, and the factors influencing successful OHL integration within healthcare settings. METHODS: Guided by PRISMA and following a predefined registered protocol (PROSPERO 2024:CRD42024537425), this systematic review analyzed studies from six key databases, using targeted keywords associated with OHL. Eligibility criteria isolated research on OHL tools, practices, and outcomes in healthcare settings. Independent reviewers conducted study selection, data extraction, and bias risk analysis. Systematic quality assessment and data extraction were performed to thoroughly evaluate OHL's impact on healthcare. RESULTS: This systematic review identified 62 articles, published between 2010 and 2024, from 15 different countries. A notable share (30.6%) aimed to develop, validate, and pilot context-sensitive OHL assessment tools. Other studies included qualitative (24.1%), descriptive (14.6%), case studies (11.29%), cross-sectional (8.06%), mixed methods (8.06%), and quantitative (3.25%) approaches, investigating factors promoting and impeding OHL outcomes. The results highlight a 54.1% increase in studies during the COVID-19 pandemic compared to the pre-pandemic period. CONCLUSION: OHL is instrumental in advancing healthcare systems towards greater accessibility and patient-centeredness. Nevertheless, overcoming the identified implementation barriers is crucial for realizing OHL's full potential in enhancing healthcare equity and efficiency. Strategic efforts are needed to foster organizational support, adapt structural practices, and allocate necessary resources for OHL initiatives to enhance 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 | 0.035 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".