Navigating Challenges in Using Interpretative Phenomenological Approach with Photovoice: A Systematic Review
Bibliographic record
Abstract
Interpretative Phenomenological Analysis (IPA) and photovoice are two qualitative research methods commonly employed in social and health research. While both are used independently to understand lived experiences, their integration is increasingly being explored to capture both narrative depth and visual expression. However, integrating these two approaches poses unique methodological challenges. A systematic review was conducted to review the methodological challenges encountered when integrating IPA with photovoice in social and health research. The study utilized a comprehensive search strategy across multiple electronic databases to identify and synthesize findings from relevant peer-reviewed articles. The review adhered to the PRISMA guidelines and focused on studies published from inception to 2024. The review identified 18 studies that addressed the methodological challenges of integrating IPA and photovoice. Combining IPA and photovoice presents several intricate methodological challenges, including participant vulnerability, recruitment and retention difficulties, interpretive complexity, the emotional and psychological impact on researchers, and the need for support systems. Strategies used to address these challenges include diverse recruitment strategies, participant training, flexibility in data collection, triangulation for methodological rigor, emotional and psychological support for researchers, cultural competence and sensitivity, community collaboration, innovative use of technology, and ensuring confidentiality and ethical considerations. This review reveals that integrating IPA with photovoice, while promising for capturing nuanced lived experiences and advancing culturally sensitive, participatory research, is accompanied by significant methodological challenges. Addressing the methodological challenges identified in this study through innovative strategies and robust ethical frameworks is essential to fully harness the potential benefits of this integrated approach in social and health research.
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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 | 0.119 | 0.307 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.032 | 0.021 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".