Last mile research: a conceptual map
Bibliographic record
Abstract
Background: The term ‘last mile’ has been used across disciplines to refer to populations who are farthest away, most difficult to reach, or last to benefit from a program or service. However, last mile research lacks a shared understanding around its conceptualization.Objectives: This project used a concept mapping process to answer the questions: what is last mile research in global health and, how can it be used to make positive change for health equity in the last mile?Methods: Between July and December 2019, a five-stage concept mapping exercise was undertaken using online concept mapping software and an in-person consensus meeting. The stages were: establishment of an expert group and focus prompt; idea generation; sorting and rating; initial analysis and final consensus meeting.Results: A group of 15 health researchers with experience working with populations in last mile contexts and who were based at the Matariki Network institutions of Queen’s University, CAN and Dartmouth College, USA took part. The resulting concept map had 64 unique idea statements and the process resulted in a map with five clusters. These included: (1) Last mile populations; (2) Research methods and approaches; (3) Structural and systemic factors; (4) Health system factors, and (5) Broader environmental factors. Central to the map were the ideas of equity, human rights, health systems, and contextual sensitivity.Conclusion: This is the first time ‘last mile research’ has been the focus of a formal concept mapping exercise. The resulting map showed consensus about who last mile populations are, how research should be undertaken in the last mile and why last mile health disparities exist. The map can be used to inform research training programs, however, repeating this process with researchers and members from different last mile populations would also add further insight.
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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 | 0.041 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.011 | 0.040 |
| Scholarly communication | 0.021 | 0.036 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".