Remote and Equitable Inductive Analysis for Global Health Teams: Using Digital Tools to Foster Equity and Collaboration in Qualitative Global Health Research via the R-EIGHT Method
Bibliographic record
Abstract
Qualitative methods encompass a variety of research and analysis techniques which have the common aim of uncovering what cannot be captured numerically through the quantification of data. For qualitative analytical methods in the interpretivist tradition (e.g. grounded theory, phenomenological, thematic, etc), inductive coding has become a mainstay but has not always lent itself to collaborative, remote team-based data interpretation among qualitative and mixed-methods clinical researchers. Finding ways to speed the inductive coding process without sacrificing rigour while remaining accessible to geographically dispersed teams remains a priority. This is especially crucial in global health partnerships where on-the-ground researchers may have less input into codebook development compared to in-the-office researchers. We describe a newly-developed, digital approach that integrates findings from our qualitative team, which we call R-EIGHT (Remote and Equitable Inductive Analysis for Global Health Teams). The technique we developed a) speeds the process of inductive coding as a team, b) visually displays interpretive consensus, and c) when appropriate fosters streamlined integration of inductive findings into codebooks. Because it involves all team members, our approach helps break the divide between in-office and on-the-ground teams, fostering integrated and representative contributions from all globally-dispersed team members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.132 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".