Body-Map Storytelling as a Health Research Methodology: Blurred Lines Creating Clear Pictures
Bibliographic record
Abstract
In this article we review the literature on body-mapping (BM) as an approach to health research in order to systematize recent advancements and to contribute to its development. We conducted a critical narrative synthesis of the literature published until September 2016 guided by two questions: 1. How has BM been utilized in health research? 2. How does BM advance a decolonization agenda? Twenty-seven studies in English, Spanish, and Portuguese were analyzed. Most of them were published between 2011 and 2016 and were conducted in South Africa, Canada, Australia, Brazil, Chile, and USA. They narrate stories of marginalized groups and commonly focus on the social determinants of health. Data generation, analysis, and knowledge mobilization strategies differ considerably. Recent developments show that body-mapping is a visual, narrative, and participatory methodology that has several names and is used unevenly by health researchers. Despite its diversity, core methodological elements reveal that participants are considered knowledgeable, reflexive individuals who can better articulate their complex life journeys when painting and drawing their bodies and social circumstances. The decolonization of health research occurs when these unlikely protagonists tell their stories producing counter-hegemonic discourses to exclusionary capitalist, patriarchal and colonialist rationalities. We call this methodology body-map storytelling.
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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 | 0.037 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".