Developing a new cross-disciplinary network to realise the potential of visualisation approaches to address healthcare associated infections
Bibliographic record
Abstract
Issue: \nA central issue in infection prevention and control work is the invisibility under normal circumstances of pathogenic organisms. Associated lack of, or delayed, feedback to clinicians on the efficacy of their IPC practice compounds the challenge for education, practice development and quality improvement. Within this context the potential for more dynamic approaches to visualising pathogens, practice and place remains under-developed. This presentation outlines key aspects of the inception of a new international network to address this issue. \n \nProject: \nThe HAIVAIRN (Healthcare Associated Infection Visualisation and Ideation Research Network) project ( http://visionon.org ) aims to explore the question: how can we better address the problem of HAIs through visualisation-related ideation and applications? Its ambit ranges from visualisation of micro, unseen phenomena such as pathogens and the mind’s eye, to visualisation of macro phenomena relating to human interactions in particular healthcare environments e.g. from aspects of the imagination through to new, scientific information (e.g. microbiological data) and related professional behaviours. Enquiry is structured around a series of workshop events with interim activities. \n \nResults: \nThis UK based network has so far coalesced expertise from medical microbiology, psychology, social geography, literature, design, nursing, cleaning services, communication, social policy and health humanities. This includes inputs from Canada and Australia, and a first workshop meeting has taken place. This established insights into how different disciplines understand and use visualisation and associated ideas, and identified areas of perceived research need and opportunity such as: mapping pathogen movement; communicating risk in context; designing interventions to influence practice; and visualising healthcare staff experiences. A set of visual mappings is currently being created to highlight loci and foci for cross-disciplinary work and two further workshops are planned. \n \nLessons learned: \nThere is much enthusiasm for breaking down disciplinary barriers and the presentation aims to further this process to expand the network.
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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 | 0.002 |
| 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".