Understanding Access to Health Care Services Through the Stories of Women Living on a Low Income: A Qualitative Secondary Data Analysis
Bibliographic record
Abstract
In 2021, more than 2.4 million women and girls in Canada were living on a low income, which is a main social determinant of health (SDH). These women are at an increased risk for health issues, including anxiety, depression, and negative health behaviours such as smoking, physical inactivity, and unhealthy eating. Despite their risk of adverse health, women living on a low income continue to face challenges with accessing health care services. Although researchers have explored access among several subgroups of women who live on a low income, few have used a narrative approach as their methodology, and none have used dialogic/performative analysis. Furthermore, no qualitative secondary data analysis (QSDA) has been done among this population in the Canadian context. The purpose of this QSDA was to critically examine the stories about access to health care services for women living on a low income in Kingston, Ontario and to explore how the SDH (e.g., housing, food, income, transportation) affect their experiences, as reflected through a theatrical metaphor. Using interview and focus group transcripts from the primary study, five narrative threads were illuminated in the stories of five participants: sacrifice and survival; feeling powerless; loss of hope; abandonment and trust; and resilience. In this study, the complexity of health care for women was demonstrated; this involves their values about health, the barriers encountered when accessing services, and the deterrents of future system use. Increased financial support for women to promote their own health at home and in the community was highlighted as an ongoing need. Within health care settings, women indicated more compassion is needed from health care workers to encourage trust and partnership. Despite the hardships they experienced, the women continued to show strength and motivation. In this study, by further examining participant transcripts, a deeper understanding of women’s access to health care in Canada while living on a low income was developed.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".