Challenges of design, implementation, acceptability, and potential for, biomedical technologies in the Peruvian Amazon
Bibliographic record
Abstract
BACKGROUND: Biomedical technologies have the potential to be advantageous in remote communities. However, information about barriers faced by users of technology in general and in remote Indigenous communities is scarce. The purpose of this study was to characterize the leading challenges faced by researchers who have used biomedical technologies in the Peruvian Amazon. METHODS: This exploratory, qualitative study with a phenomenological approach depicts the lived experience of participants who were researchers with experience working with biomedical technologies in the Peruvian Amazon in the past five years. Analysis was based on three core themes: design, implementation, and acceptability. Sub-themes included environment, community, and culture. Of the 24 potential participants identified and contacted, 14 agreed to participate, and 13 met inclusion criteria and completed semi-structured interviews. Results were sent to each participant with the opportunity to provide feedback and partake in a 30-minute validation meeting. Five participants consented to a follow-up meeting to validate the results and provide further understanding. RESULTS: Participants recognized significant challenges, including technologies designed out-of-context, difficulty transporting the technologies through the Amazon, the impact of the physical environment (e.g., humidity, flooding), and limited existing infrastructure, such as electricity and appropriately trained health personnel. Participants also identified cultural factors, including the need to address past experiences with technology and health interventions, understand and appropriately communicate community benefits, and understand the effect of demographics (e.g., age, education) on the acceptance and uptake of technology. Complementary challenges, such as corruption in authority and waste disposal, and recommendations for technological and health interventions such as co-design were also identified. CONCLUSIONS: This study proposes that technological and health interventions without efforts to respect local cultures and health priorities, or understand and anticipate contextual challenges, will not meet its goal of improving access to healthcare in remote Amazon communities. Furthermore, the implications of corruption on health services, and improper waste disposal on the environment may lead to more detrimental health inequities.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".