Understanding system barriers and facilitators in transnational clinical cancer research: The value of rapid and multimodal ethnographic inquiry
Bibliographic record
Abstract
Introduction In middle and low resource countries worldwide, up to 70% of breast cancer cases are diagnosed as locally advanced (stages IIB-IIIC). Delays in referral from primary to specialty care have been shown to prolong routes to diagnosis and may be associated with higher burdens of advanced disease, but specific clinical and organizational barriers are not well understood. Methods This article reports on the use of rapid ethnographic research (RER) within a largescale clinical trial for locally advanced breast cancer (LABC) in India, Mexico, South Africa, and the US. Our purpose is twofold. First, we demonstrate the value of ethnography as a mode of evaluative listening: appraising the perspectives of diverse patients and clinicians regarding prolonged routes to LABC diagnosis and treatment. Second, we show the value of ethnography as a compass for navigating among discrepant clinical research styles, IRB protocols, and institutional norms and practices. We discuss advantages and limits involved in each use of RER. Results On the one hand, ethnographic interviews carried out before and during the clinical trial enabled more regular communication among investigators and research sites. On the other hand, the logistics of doing the trial placed limits on the extent and duration of inductive, immersive inquiry characteristic of traditional fieldwork. As a partial solution to this problem, we developed a multimodal ethnographic research (MER) approach, an augmentation of video-chat, phone, text, and email carried out with, and built upon the initial connections established in, the in-person fieldwork. This style has its limits; but it did allow us to materially improve the ways in which the medical research proceeded. Discussion In conclusion, we highlight the value of not deferring to a presumed incommensurability of ethnographic fieldwork and clinical trialwork while still being appropriately responsive to moments when the two approaches should be kept apart.
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 | 0.114 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| 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".