The mental health of survivors of violence against women who accessed supportive services during the COVID-19 pandemic: A narrative thematic analysis
Bibliographic record
Abstract
BACKGROUND: Violence against women (VAW) is a pervasive public health problem in Canada with detrimental impacts on the mental health of survivors. The COVID-19 pandemic led to an increase in the incidence and severity of VAW, deterioration in population level mental health and well-being, and exacerbated barriers to accessing health and social services. People who were already vulnerable to mental illness or people experiencing marginalization across social factors experienced even greater challenges with their mental health. OBJECTIVES: We aimed to understand the mental health of VAW survivors accessing services during the pandemic and how experiences differed across diverse life histories and sociodemographic factors. DESIGN: We conducted interviews from April to September 2021, with 10 adult women who had accessed at least one VAW service in the Greater Toronto Area since March 11, 2020. These data were collected as part of a community-based study on the processes, experiences, and outcomes of adapting VAW programming during the COVID-19 pandemic. Participants were sampled through staff contacts at VAW organizations to represent a diverse cross-section of sociodemographic factors and types of services accessed. METHODS: We used narrative thematic analysis to analyze our interview data and identified how life histories and sociodemographic factors intersected with themes about their mental health. RESULTS: The research team identified four narrative themes pertaining to survivor mental health: (1) new and exacerbated anxiety, depression, and substance use, (2) feelings of hopelessness and mental exhaustion, (3) shame and low self-esteem, and (4) resiliency. Survivor's experiences across these themes differed based on personal factors and life histories (e.g. being a newcomer, being a mother, experiences of childhood trauma and abuse, living with a disability, and socioeconomic status). CONCLUSION: During the pandemic, survivors experienced greater mental health needs and at the same time encountered greater challenges in accessing support, which had significant consequences for their mental well-being. Services that support VAW survivors (as essential services) require increased funding and resources to offer effective, accessible, and timely support that improves the lives of survivors. This support must consider survivors' unique needs based on personal factors and life histories during and beyond public health emergencies.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".