Are we prepared? The development of performance indicators for public health emergency preparedness using a modified Delphi approach
Bibliographic record
Abstract
BACKGROUND: Disasters and emergencies from infectious diseases, extreme weather and anthropogenic events are increasingly common. While risks vary for different communities, disaster and emergency preparedness is recognized as essential for all nation-states. Evidence to inform measurement of preparedness is lacking. The objective of this study was to identify and define a set of public health emergency preparedness (PHEP) indicators to advance performance measurement for local/regional public health agencies. METHODS: A three-round modified Delphi technique was employed to develop indicators for PHEP. The study was conducted in Canada with a national panel of 33 experts and completed in 2018. A list of indicators was derived from the literature. Indicators were rated by importance and actionability until achieving consensus. RESULTS: The scoping review resulted in 62 indicators being included for rating by the panel. Panel feedback provided refinements to indicators and suggestions for new indicators. In total, 76 indicators were proposed for rating across all three rounds; of these, 67 were considered to be important and actionable PHEP indicators. CONCLUSIONS: This study developed an indicator set of 67 PHEP indicators, aligned with a PHEP framework for resilience. The 67 indicators represent important and actionable dimensions of PHEP practice in Canada that can be used by local/regional public health agencies and validated in other jurisdictions to assess readiness and measure improvement in their critical role of protecting community health.
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 | 0.270 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".