Preventive strategies in health promotion for ensuring the right to health
Bibliographic record
Abstract
The right to health, as defined by the World Health Organization (WHO), is the right of every individual to access the highest attainable standard of health, encompassing both access to healthcare and the broader social determinants of health such as health literacy, clean water, and healthy environments. Health promotion, as a ‘process of enabling people to increase control over their health’ (Ottawa charter, 1986), plays a vital role in realizing this right. It involves addressing both individual behaviors and social, economic, and environmental factors that influence health, through education, policies, and community-based interventions. Health promotion strategies, such as WHO Healthy Cities initiatives, healthy nutrition, physical activity, oral health, tobacco and alcohol control, mental health promotion, the appropriate use of healthcare services, ecology and health, immunization, and preventive screenings – are integral to fulfilling the right to health. Special attention is given to gender-sensitive preventive strategies, particularly those focused on access to prenatal care, adolescent’s and women’s health, reproductive health, and healthy ageing. This paper highlights the experiences of health promotion interventions (programmes and projects) in Novi Sad and the Autonomous Province of Vojvodina, developed by the Institute of Public Health of Vojvodina, focusing on key mentioned areas. These programs aim to improve health outcomes by engaging communities in healthier lifestyle choices and behaviors. Health promotion efforts in the City of Novi Sad and the Autonomous Province of Vojvodina (APV) offer valuable examples of how local interventions, tailored to the specific needs of the population, can reduce health risks and improve outcomes. By examining case studies and best practices, the paper illustrates the successes and challenges of these initiatives, stressing the importance of continuous evaluation of their impact. Finally, the paper proposes policy recommendations for strengthening preventive strategies at local, national, and international levels, advocating for the development of comprehensive legal frameworks to ensure equitable access to preventive health services for all populations. These strategies demonstrate how health promotion contributes to health equity, empowering individuals and communities to make healthier choices and ultimately improving population health outcomes.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 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 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".