Using Photovoice as a participatory approach to promote youth health literacy
Bibliographic record
Abstract
Health literacy is critical to empowerment (Kickbusch, 2005) as it entails the knowledge, motivation and competence to access, understand, appraise and apply information to form judgements and make decisions in everyday life (Sørensen et al, 2012; see Chapter 1, this volume). It encapsulates healthcare, disease prevention and health promotion to maintain and promote the quality of life. Health literacy goes beyond the narrow concept of health education and individual behaviour-oriented communication by addressing the environmental, political and social factors that determine health (WHO, 2016; see also Chapters 11 and 14, this volume). Understanding health literacy, in turn, contributes to an in-depth comprehension of health education, aiming to influence individual lifestyle decisions and enhance the awareness of the determinants of health through methods that go beyond campaigns and information sharing (WHO, 2016). Health literacy emphasises social participation (see Chapters 40 and 44, this volume) and critical thinking, leading to enhanced health literacy with personal and social benefits as well as community action that supports the development of social capital (WHO, 2016). We need a shift in how we perceive, measure and design interventions to incorporate the comprehensive understandings of health, health literacy and social participation across the lifespan. This chapter presents a case study related to the use of Photovoice to promote youth health literacy. The potential challenges and benefits of using Photovoice, along with future recommendations that arose from conducting the case study, are discussed in the following sections. The concept of health literacy, embedded in modern-day health promotion practices (Stewart et al, 2008), is grounded in the principles of empowerment, human rights, ethics, values and equity, in accordance with the Ottawa Charter for health promotion (Lindström and Eriksson, 2011).
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".