The Barriers to Accessing Health Care for Women Previously Trafficked: Scoping Review
Bibliographic record
Abstract
The stereotyping of the experiences of women and sensational imagery of anti-trafficking awareness campaigns restricts the efforts of healthcare providers to address the needs of women previously trafficked. The purpose of this scoping review was to explore the barriers to accessing health care for women previously trafficked. A scoping review was conducted to assess the initial breadth of available research. In early 2021, the titles, abstracts and subject headings were searched in CINAHL Plus with Full Text, Medline, Embase, SocINDEX with Full Text, Scopus, and Psych Info. The resulting articles were screened by two reviewers based on established inclusion and exclusion criteria. Conflicts were resolved through conversations between the two reviewers. The overall search yielded a total of 1241 records after duplicates were removed. A total of 12 full-text articles were included in this review. Barriers included control by trafficker(s), lack of documentation, and previous negative experiences with healthcare and service providers. There was a lack of congruency with identifying self-reliance and self-treatment as a barrier or facilitator. Recommended policy changes include less prosecution-orientated approaches, a focus on social policy for the protection of women, and poverty alleviation. Peer-to-peer support can facilitate access to health care for women previously trafficked.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".