Advancing Performance Measurement for Public Health Emergency Preparedness: An Integrated Knowledge Translation Approach
Bibliographic record
Abstract
Introduction: Public health emergency management involves the timely translation of relevant evidence and effective coordination of diverse actors. In practice, this can be challenging in the absence of a common framework for action among diverse actors. Aim: To apply an Integrated Knowledge Translation (iKT) approach throughout the development of a conceptual framework and performance measurement indicators for public health emergency preparedness (PHEP), to ensure knowledge generated is relevant and useful to the field. Methods: The iKT approach was initiated by identifying a research question based on priorities from the field. The two phases of the study used participatory research methods as well as active engagement with potential end users at key study milestones. The Structured Interview Matrix (SIM) facilitation technique for focus groups and an expert panel using Delphi methodology were used to define the PHEP framework and performance measurement indicators, respectively. An advisory committee was assembled consisting of potential end-users of the research, in senior positions in applied and decision-making roles. Results: iKT was an essential component for this applied public health project, contributing to and enhancing the relevance of the knowledge generated. iKT contributed to the following: broad national engagement and interest in the study, successful recruitment in both phases, and engagement with decision-makers. This multi-dimensional participatory approach successfully generated knowledge that was important to the field demonstrated by relevance to practice and policy in jurisdictions across Canada. Furthermore, the approach fostered building resilience in local and national communities through collaboration. Discussion: The iKT approach was essential to generating knowledge that is relevant and useful to the field, mainly to promote health system preparedness and resilience. Future research to study the implementation of knowledge will be important to continue addressing the knowledge-to-action gap in health emergency management research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.186 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".