The Importance of Contextual Understanding in Solving the Last Mile Problem
Bibliographic record
Abstract
The COVID-19 pandemic has disrupted many global industries and shifted the digital health landscape by stimulating and accelerating the delivery of digital care. It has emphasized the need for a system level informatics implementation that supports the healthcare management of populations at a macro level while also providing the necessary support for front line care delivery at a micro level. From data dashboard to Telemedicine, this crisis has necessitated the need for health informatics transformation that can bridge time and space to provide timely care. However, heath transformation cannot solely rely on Health Information Technology (HIT) for progress, but rather success must be an outcome of system design focus on the contextual complexity of the health system where HIT is used. This conference highlights the important roles context plays for health informatics in global pandemics and aims to answer critical questions in four main areas: 1) health information management in the covid-19 context, 2) implementation of new practices and technologies in healthcare, 3) sociotechnical analysis of task performance and workload in healthcare, and 4) innovations in design and evaluation methods of health technologies. We deem this as a call to action to understand the importance of context while solving the last mile problem in delivering the informatics solutions that are needed to support our public health response.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".