Community-Based Workers’ Health and Wellness: Supporting Women Survivors of Intimate Partner Violence
Bibliographic record
Abstract
An individual’s working conditions are a key social determinant of health. Community-based workers (CWs) provide vital support to survivors of intimate partner violence (IPV); they are directly and repeatedly involved in serving those who experience harmful acts of violence, which may subsequently affect health. By using a salutogenic orientation, this qualitative descriptive inquiry explores the health and wellness of CWs employed in non-profit settings who support women survivors of IPV in the Niagara Region, Ontario. A total of 19 CWs (n = 19) from four organizations participated in individual semi-structured interviews and thematic analysis generated five interconnected themes: (1) mental processing and alternations includes how CWs internalized their work from processing women’s trauma which interfered with their way of being; (2) unmanageable structural challenges considers the challenges that were not within the CWs’ control which influenced their ability to view their work as manageable and pushed their health and wellness toward the ill end of the health continuum; (3) women empowering women encapsulates how women empowerment was shared between female CWs who supported women survivors, and how CWs’ work enhanced health and wellness by providing them with a sense of meaning and inspiration to self-reflect and grow from their personal struggles; (4) unique ways of coping describes CWs’ personal ways of coping which assisted them in dealing with stressful work-related experiences; and (5) recommendations for system improvements which captures CWs’ recommendations for structural changes to promote health and wellness. The study contributes knowledge to the IPV field as the findings demonstrated several ways in which supporting survivors influenced the health and wellness of CWs in non-profit organizations in the Canadian context. The findings have implications for nurses to be leadersin raising public awareness, advocacy, and promoting better health outcomes for CWs. They also have implications for strategies involving intersectoral collaboration as a means to promote community awareness about CWs and the services they facilitate, and to support the needs of the IPV CW workforce.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".