Using Ethno-Epidemiology in a Prospective Observational Study to Increase the Rigour of Nested Qualitative Research
Bibliographic record
Abstract
Ethnographic-epidemiological (“ethno-epi”) research methodologies are increasingly being used to examine health-related issues, including the experiences of people who use drugs. However, the complementary application of random sampling from a well characterised cohort and qualitative data collection methods in a single study has not been described. We address this gap by sharing insights from the implementation of a novel random stratified sampling technique to recruit participants from two large prospective observational studies of people who use drugs into a qualitative study about impacts of the COVID-19 pandemic on their lived experience. We aim to describe how an ethno-epi approach we used can enhance the validity, reliability and generalizability of research findings in mixed methods investigations. We do so by providing a step-by-step description of the process we used to determine participant eligibility and recruitment into the qualitative study. Although the approach is not without limitations, findings underscore how ethno-epi random sampling approaches can increase the credibility and trustworthiness of qualitative findings without compromising data depth and integrity. Our study makes an important contribution to the growing number of new creative approaches being developed in the mixed methods research field and we hope that by sharing our account it will encourage and support others to consider the use of ethno-epi approaches in health-related research.
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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 | 0.342 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".