Addressing the effectiveness of health literacy programs within the Gulf Corporation Council: an integrative review
Bibliographic record
Abstract
Health literacy is an increasingly required need to help individuals, families and communities manage their health and health conditions. It is linked with better self-adherence to treatments, use of resources, access to care and overall reduced costs in healthcare. In the Gulf Cooperation Council (GCC), which comprises Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates, various health literacy programs are implemented across states to address people's unique and complex healthcare needs. This article aims to examine the current literature and assess the factors that influence the outcomes of health literacy programs within the GCC. An integrative review methodology has been conducted to pursue a comprehensive understanding of health literacy interventions in the GCC. This investigative approach was shaped by Whittemore and Knafl's framework (2005), which includes problem identification, literature search, data evaluation, data analysis and presentation. The literature on the effectiveness of health literacy interventions and the factors that shape them are notably limited worldwide and within the GCC region. This integrative review addresses this knowledge gap and highlights the significance of key themes such as sessions, evaluation and improvement in shaping health literacy outcomes within the GCC region. Through this integrative review, the three main themes of sessions, evaluation and improvement were identified as influencing the outcomes of health literacy programs within the GCC.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".