Integrated Management Systems (IMS) to Support and Sustain Quality One Health Services: International Lessons from the COVID-19 Pandemic by the IMIA Primary Care Working Group
Bibliographic record
Abstract
OBJECTIVES: One Health considers human, animal and environment health as a continuum. The COVID-19 pandemic started with the leap of a virus from animals to humans. Integrated management systems (IMS) should provide a coherent management framework, to meet reporting requirements and support care delivery. We report IMS deployment during, and retention post the COVID-19 pandemic, and exemplar One Health use cases. METHODS: Six volunteer members of the International Medical Association's (IMIA) Primary Care Working Group provided data about any IMS and One Health use to support the COVID-19 pandemic initiatives. We explored how IMS were: (1) Integrated with organisational strategy; (2) Utilised standardised processes, and (3) Met reporting requirements, including public health. Selected contributors provided Unified Modelling Language (UML) use case diagram for a One Health exemplar. RESULTS: There was weak evidence of synergy between IMS and health system strategy to the COVID-19 pandemic. However, there were rapid pragmatic responses to COVID-19, not citing IMS. All health systems implemented IMS to link COVID test results, vaccine uptake and outcomes, particularly mortality and to provide patients access to test results and vaccination certification. Neither proportion of gross domestic product alone, nor vaccine uptake determined outcome. One Health exemplars demonstrated that animal, human and environmental specialists could collaborate. CONCLUSIONS: IMS use improved the pandemic response. However, IMS use was pragmatic rather than utilising an international standard, with some of their benefits lost post-pandemic. Health systems should incorporate IMS that enables One Health approaches as part of their post COVID-19 pandemic preparedness.
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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 | 0.067 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".