Mental Illness in Refugee and Immigrant Women: A Midwife's Perspective on Culturally Competent Care
Bibliographic record
Abstract
The assessment, diagnosis and referral for mental illness management have been recognized as indispensable roles within midwifery practice both in the United States and in Canada. War, civil unrest and natural disasters, have made mental health assessment crucial for refugee and new immigrant women who present as midwifery clients. Since 1991, National Depression Screening Day (NDSD) had been conducted in cities across the United States and Canada using the Harvard Department of Psychiatry National Depression Screening Day Scale (HANDS©). It facilitates the diagnosis of depression based on DSM-IV criteria. The HANDS© tool includes a 10-item interview guide which is a “self-report scale”. HANDS© demonstrated good internal consistency and validity and was 95 percent sensitive.33 Responses could indicate the need for thyroid screening, referral for mental health counselling or psychiatric care. Strategies for transforming midwifery practice are explored: taking a holistic approach, communication strategies, continuing education strategies, documentation and empowerment issues, multidisciplinary team approach, clinic environment issues, resources and referral base. As we develop expertise in the care of torture and trauma survivors, it behooves us to identify the experts amongst our ranks and to refer our clients to these midwives who will best meet their needs. Further research is needed to guide our incorporation of the skills for treating survivors in our educational programs for the midwives of 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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".