ILLUMINATING SOCIAL DETERMINANTS OF WOMEN'S HEALTH USING GROUNDED THEORY
Bibliographic record
Abstract
Emphasis in health policy has shifted from curative intervention to prevention and health promotion through personal responsibility for lifestyle choices and, most recently, to the social determination of health. These shifts draw attention to and legitimize women's health research that moves beyond biomedical, epidemiological, and subjective knowledge to question previously unquestioned societal norms and structures that influence women's health. The challenge is to avoid relying solely on population-based studies that support relationships between social determinants and indicators of women's health and to find ways to illuminate the processes by which social determinants interact with the health of specific groups of women. Without such research, our knowledge of how social factors that underpin women's health interact will be faceless and will not address the interplay of health and social policy within women's lives. One research method that may be useful for exploring the interplay between such policies and women's health is grounded theory. Grounded theory is a widely used approach in women's health research. The goal of grounded theory is the discovery of dominant social and structural processes that account for most of the variation in behavior in a particular situation. Despite the usefulness of this method for capturing the interaction between social conditions and women's health experiences, many grounded theory researchers restrict themselves to women's subjective experiences as a source of data for theory development. Consequently, the resultant theory's capacity to illuminate the effects of the social determinants of health is limited. The purpose of this article is to discuss how the grounded theory method can be used in a participatory way to theoretically sample structural conditions at many levels. Using examples from completed and ongoing women's health research where data have and have not been collected primarily from women themselves, we outline the benefits and process for using grounded theory to influence health and public policy in women's health.
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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 | 0.057 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".