Spiraling Between Qualitative and Quantitative Data on Women's Health Behaviors: A Double Helix Model for Mixed Methods
Bibliographic record
Abstract
A double helix spiral model is presented which demonstrates how to combine qualitative and quantitative methods of inquiry in an interactive fashion over time. Using findings on women's health behaviors (e.g., menstruation, breast-feeding, coping strategies), we show how qualitative and quantitative methods highlight the theory of knowledge acquisition in women's health decisions. A rich data set of 48 semistructured, in-depth ethnographic interviews with mother-daughter dyads from six ethnic groups (Israeli, European, North African, Former Soviet Union [FSU], American/Canadian, and Ethiopian), plus seven focus groups, provided the qualitative sources for analysis. This data set formed the basis of research questions used in a quantitative telephone survey of 302 Israeli women from the ages of 25 to 42 from four ethnic groups. We employed multiple cycles of data analysis from both data sets to produce a more detailed and multidimensional picture of women's health behavior decisions through a spiraling process.
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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 | 0.590 | 0.559 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".