Experience of Vulnerable Women Narrated through the Body-Mapping Technique
Bibliographic record
Abstract
Vulnerable women are considered a priority in public policies and research agendas. It is necessary to understand better the specificities of their daily lives and the meanings they attribute to their experiences, as this undoubtedly contributes to more grounded and culturally appropriate practices. Additionally, innovative techniques in qualitative research are demanded in academia. This narrative research study was carried out with fourteen women from a Brazilian socioeconomically vulnerable neighborhood. We used the body-mapping technique to investigate the experiences of women with mental health disorders or psychosocial distress. The aim was to analyze the self-perception about daily stressors and discuss the feasibility of this technique to facilitate this group's storytelling. Data collection was performed through focus groups, guided by the body-mapping technique steps, and supplemented with individual interviews. Interpersonal conflicts and violence were the main stressors. These strongly impacted the well-being of these women and their children. Some important personal qualities and resilience were identified. Body-mapping played a fundamental role in facilitating storytelling. It amplified the linguistic possibilities for participants to express their feelings and promoted reflections about the present, past, and glimpses into the future.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".