Introducing SAMMSA, a Five-Step Method for Producing ‘Quality’ Qualitative Analysis
Bibliographic record
Abstract
Qualitative health research is ever growing in sophistication and complexity. While much has been written about many components (e.g. sampling and methods) of qualitative design, qualitative analysis remains an area still needing advanced reflection. Qualitative analysis often is the most daunting and intimidating component of the qualitative research endeavor for both teachers and learners alike. Working collaboratively with research trainees, our team has developed SAMMSA (Summary & Analysis coding, Micro themes, Meso themes, Syntheses, and Analysis), a 5-step analytic process committed to both clarity of process and rich ‘quality’ qualitative analysis. With roots in hermeneutics and ethnography, SAMMSA is attentive to data holism and guards against the data fragmentation common in some versions of thematic analysis. This article walks the reader through SAMMSA’s 5 steps using research data from a variety of studies to demonstrate our process. We have used SAMMSA with multiple qualitative methodologies. We invite readers to tailor SAMMSA to their own work and let us know about their processes and results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.563 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.020 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".