Body mapping as a site to negotiate eating struggles and food insecurity for street-involved and homeless youth
Bibliographic record
Abstract
Inspired by critical trauma and embodiment theories, this study aims to illustrate how an arts-based approach such as body mapping assists in exploring the lived experiences of youth, potentially serving as a trauma-informed approach. This qualitative study collaborated with street-involved and homeless youth (SIHY) who have eating struggles while living in situations of food insecurity and other forms of oppression. Eleven participants partook in three individual face-to-face interview sessions and one arts-based body map activity, respectively, at a local SIHY resource centre in a metropolitan city in Canada. Guided by Interpretative Phenomenological Approach (IPA), our findings illustrated how body mapping (1) enabled a deepened understanding of SIHY’s eating struggles as both a form of suffering and an embodied means of coping with food insecurity and other systemic and relational trauma(s); (2) provided a transformative experience leading to greater self-compassion and healing; and (3) served as a trauma-informed method that fostered choice and validation. We attest that, as a creative and supportive clinical and research tool, body mapping taps into the unspoken, expressive, embodied, and somatic aspects of eating struggles, food insecurity, poverty, and other forms of oppression deepening knowledge and informing social work research and practice.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".