The Role Nurses Can Play in Addressing and Preventing the Prevalence of Missing or Murdered Indigenous Women and Girls (MMIWG)
Bibliographic record
Abstract
INTRODUCTION: In 2016, 5,712 American Indian/Alaskan Native (AI/AN) women and girls were reported missing in the United States. In Canada, 4% of the population is Indigenous, yet Indigenous females represent 50% of all sex trafficking victims. This systematic mixed-studies review examined the effects of Missing and Murdered Indigenous Women and Girls (MMIWG) to define a role for nurses. METHODS: We used five databases with keywords, inclusion criteria, and the Mixed Methods Appraisal Tool. RESULTS: Findings of 22 papers discuss: (a) demographic data; (b) factors that increase vulnerability of AI/AN women; and (c) how nurses can decrease the prevalence of MMIW. DISCUSSION: Nurses are the first provider patients see when accessing care. Increasing knowledge about the impact of violence against AI/AN women and girls is the first step in identifying measures needed to address this public health concern.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".