‘Welfare moms and welfare bums’: Revisiting poverty as a social determinant of health
Bibliographic record
Abstract
In the last two decades health researchers have paid increasing attention to the social determinants of health and health inequalities. Broadly, two hypotheses attempt to explain health inequalities – the materialist hypothesis and the psychosocial hypothesis. The purpose of this study was to examine the relationship between poverty and women’s health from the perspectives of a group of poor women. Our qualitative study with 20 diverse women on low-income included 32 one-on-one interviews, 15 group meetings, and 30 sets of field notes. We used the analysis program Atlas.ti to sort, code, and conduct a content analysis. Overall, our findings revealed that both hypotheses were deeply connected with the dominant ideology of poverty and the concomitant social construction of ‘welfare bum’ and ‘welfare mom’. Socioeconomic factors limited the women’s access to health promoting resources and influenced their health behaviours (such as what they ate and how much they exercised). Ideologies that promulgated negative stereotypes legitimized the systemic barriers the women faced, enforced their material scarcity, and limited their entitlements to health-promoting services and resources. Our findings also indicated that the stereotype led the women to feel shamed, stressed, and depressed, and to adopt negative health behaviors as a way of coping and finding comfort.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".