A Narrative Inquiry into the Experiences of Health and Well-Being of Women Previously Trafficked
Bibliographic record
Abstract
Using narrative inquiry, I inquired into the experiences of health and well-being of women previously trafficked in Canada. Narrative inquiry is considered a research methodology and a method of understanding human experiences as storied phenomena under study (Clandinin & Connelly, 2000). By engaging in monthly visits and ongoing conversations over nine months, the participants and I slowly co created a relational space where we co composed stories that reflected their experiences and our relationship. As we lived alongside each other, the entanglement of our stories shaped ways to inquire into experiences. Drawing on the experiences of three participants, T, Wolfie, and Phoenix, made visible the complexities and the multiplicity of these entanglements. Their experiences of health and well-being have brought forward insights into the dominant narratives about identities that are based on preconceived notions. Their experiences challenge the politics of pity and risk-management strategies within anti-trafficking strategies in Canada. They call forth the need for attentiveness as they seek narrative coherence in their lives amidst liminal spaces and silences. As they told some of their stories without words, I was called to think about who I am in requiring that their silence be broken to understand their meanings of health and well-being. By retelling and reflecting on the stories that they shared in our conversations, I identified two narrative threads that make known the distinct entanglements of their experiences. Attending closely to their lives brought forward the personal, practical, and social significance of this work, which has implications for advancing nursing knowledge and practice and the social responsibilities we hold as people and nurses in the everyday encounter with women who have been previously trafficked.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".