Bibliographic record
Abstract
The British Columbia Ministry of Health's Framework for Core Functions in Public Health was the catalyst that inspired this review of best practices in health emergency management. The fieldwork was conducted in the fall of 2005 between hurricane Katrina and the South Asia earthquake. These tragedies, shown on 24/7 television news channels, provided an eyewitness account of disaster management, or lack of it, in our global village world. It is not enough to just have best practices in place. There has to be a governance structure that can be held accountable. This review of best practices lists actions in support of an emergency preparedness culture at the management, executive, and corporate/governance levels of the organization. The methodology adopted a future quality management approach of the emergency management process to identify the corresponding performance indictors that correlated with practices or sets of practices. Identifying best practice performance indictors needed to conduct a future quality management audit is described as reverse quality management. Best practices cannot be assessed as stand-alone criteria; they are influenced by organizational culture. The defining of best practices was influenced by doubt about defining a practice it is hoped will never be performed, medical staff involvement, leadership, and an appreciation of the resources required and how they need to be managed. Best practice benchmarks are seen as being related more to "measures" of performance defined locally and agreed on by 2 or more parties rather than to achieving industrial standards. Relating practices to performance indicators and then to benchmarks resulted in the development of a Health Emergency Management Best Practices Matrix that lists specific practice in the different phases of emergency management.
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 | 0.073 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".