Enhancing inter‐organizational collaboration for wildlife disease surveillance in Sri Lanka
Bibliographic record
Abstract
Management of zoonotic infectious diseases is an urgent global heath imperative. Interdisciplinary approaches for zoonosis management exist in literature, but collaboratively implementing them is a pervasive challenge. The Sri Lanka Wildlife Health Centre (SLWHC) was created in 2011 to coordinate wildlife disease surveillance and response among government agencies. We interviewed SLWHC-affiliated personnel about existing communication and collaboration channels to identify operational needs as well as potential enhancements for the SLWHC's operations. We used the Policy Sciences' analytical framework to identify opportunities and challenges for the SLWHC. Study participants held both human and animal health as the utmost priorities. However, their observations indicate that inter-organizational communication barriers and intra-organizational hierarchies still need to be overcome for the Centre's partnering organizations to collaborate to their fullest potential. Any interventions to enhance the SLWHC's collaborative capacity for detecting and managing zoonotic disease outbreaks could be strengthened by appealing to participants' shared value orientations towards enlightenment and respect. A common interest was the desire to collaborate and combine resources, knowledge and personnel to detect, reduce and prevent the incidence of zoonotic disease outbreaks in Sri Lanka. These lessons about institutionalizing communication have considerable relevance for organizational responses to the current SARS-CoV2 pandemic and other zoonoses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".