Creating safe relational space: Public health nurses work with mothering refugee women
Bibliographic record
Abstract
OBJECTIVE: Exploring how public health nurses (PHNs) provide community-based support to women who are refugees and mothering. DESIGN: A constructivist grounded theory (CGT) design was used where intersectionality as an analytical tool was applied. Varying data collection approaches including focus groups were used. SAMPLE: Twelve PHNs from four public health units in Western Canada participated in this study. RESULTS: Participants in this study described an overall process of creating safe relational space to address a basic social problem of establishing trust while managing structural forces. This overarching process was expressed through burning with passion, connecting while looking beyond, protecting from re-traumatization, and fostering independence. Findings reveal strategies PHNs used to enhance health equity. This study extends critical caring theory to include sociopolitical and economic influences on public health nursing practice. Consequences of these influences on the mothering refugee women population are also revealed. Implications include structural integration of trauma-and-violence-informed principles to support public health nursing practice. CONCLUSIONS: This study adds to an emerging body of knowledge on PHNs work with complex populations. Innovative application of intersectionality is demonstrated as an effective approach to analyzing impacts of broad sociopolitical priorities on communities that are systemically marginalized.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".