Aligning valid research outcomes with stakeholder values—what do they need for decision-making?
Bibliographic record
Abstract
This paper is derived from a presentation given by the first author at the 2024 Symposium for the Calvin Schwabe Award, presented to Dr. Jan Sargeant for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine. Researchers must work toward ensuring validity throughout the research process, but we also should ensure that our resulting outcomes are specified to appropriately inform and enable decision-making by the end-users. Given the scope and diversity of topics addressed by veterinary researchers, the potential beneficiaries or stakeholders of our research also varies. Stakeholders or end-users may include veterinary practitioners, other researchers, livestock owners, "pet parents," government officials, corporate entities, or the general public in the case of public health or food security and safety issues. Current research in animal agriculture provides an opportunity to consider research outcomes in a sustainability framework which concurrently values social, economic, and environment impacts of animal health and management decisions. In companion animals, contemporary issues of affordability and access to care, quality of life, or compliance effects on efficacy, also extend the spectrum of relevant research outcomes. In these cases, traditional measures of animal health, such as morbidity, mortality, or weight gain, may not be the most relevant for the end-users. Furthermore, if studies are not designed and analyzed with well-defined primary outcomes that are informed by stakeholders' values, but rather post-hoc considerations of these values are made based on indirect or surrogate measures, there is the potential to incorporate error and bias into our conclusions and the end-users' decision-making processes.
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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 | 0.824 | 0.881 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.014 | 0.088 |
| Scholarly communication | 0.066 | 0.089 |
| Open science | 0.014 | 0.032 |
| Research integrity | 0.040 | 0.046 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".