Missed opportunities, human trafficking and modern day slavery: Do midwives know what this really means and why it is important to our practice
Bibliographic record
Abstract
Until recently I was ignorant of the broader definitions, the impact and breadth of human trafficking internationally. At the recent International Confederation of Midwives (ICM) Congress in Canada I had the privilege of listening to a presentation by Debra Bick about NHS maternity care response to human trafficking in the UK, the views of women and clinicians. The thought that women presenting to maternity services in Australia may actually be victims or survivors of human trafficking provoked me to learn more. Whilst some of the findings in Debra Bick's presentation were in preparation for publication, the team of colleagues at Kings College, London have multiple peer reviewed publications highlighting the health impacts and responsibilities of health professionals including midwives in this important topic.
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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 | 0.016 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".