The Impact of the Ecosystem on Health Literacy Among Rural Communities in Protected Areas: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Protected areas are crucial for the maintenance of human health and well-being. They aim to preserve biodiversity and natural resources to secure various ecosystem services that are beneficial to human health. Their ecological characteristics can influence local health literacy. Typically, communities surrounding protected areas have limited economic opportunities due to restriction policies to protect the ecosystem, resulting in socioeconomic disparities. The local community faces obstacles in gaining access to health care facilities and health information due to these limitations. It is difficult for them to locate, comprehend, and apply information and services to make better health-related decisions for themselves and others. OBJECTIVE: This study protocol examines the impact of the ecosystem on health literacy among rural communities in protected areas. METHODS: This study comprises 5 phases. In phase 1, we conduct a systematic review to identify the issue of health literacy in protected areas. In phase 2, we will collect data from stakeholders in a protected area of Pahang National Park and analyze the results using Net-Map analysis. In phase 3, we will conduct a survey among the adult community in Pahang National Park related to health literacy, socioeconomic status, health expenditure, and quality of life. In phase 4, informed by the results of the survey, we will determine suitable intervention programs to improve health literacy through a focus group discussion. Finally, in phase 5, we will conduct a costing analysis to analyze which intervention program is the most cost-effective. RESULTS: This study was funded by Universiti Sains Islam Malaysia (USIM) and strategic research partnership grants, and enrollment is ongoing. The first results are expected to be submitted for publication in 2024. CONCLUSIONS: This is one of the first studies to explore health literacy among rural communities in protected areas and will provide the first insights into the overall level of health literacy in the protected community, potential determinants, and a suitable intervention program with expected cost analysis. The results can be used to promote health literacy in other protected areas and populations. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number Registry ISRCTN40626062; http://tinyurl.com/4kjxuwk5. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/51851.
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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 | 0.080 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.010 |
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".