Assessing healthcare capacity crisis preparedness: development of an evaluation tool by a Canadian health authority
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic presented health systems across the globe with unparalleled socio-political, ethical, scientific, and economic challenges. Despite the necessity for a unified, innovative, and effective response, many jurisdictions were unprepared to such a profound health crisis. This study aims to outline the creation of an evaluative tool designed to measure and evaluate the Vitalité Health Network's (New Brunswick, Canada) ability to manage health crises. Methods: The methodology of this work was carried out in four stages: (1) construction of an evaluative framework; (2) validation of the framework; (3) construction of the evaluative tool for the Health Authority; and (4) evaluation of the capacity to manage a health crisis. Results: The resulting evaluative tool incorporated 8 dimensions, 74 strategies, and 109 observable elements. The dimensions included: (1) clinical care management; (2) infection prevention and control; (3) governance and leadership; (4) human and logistic resources; (5) communication and technologies; (6) health research; (7) ethics and values; and (8) training. A Canadian Health Authority implemented the tool to support its future preparedness. Conclusion: This study introduces a methodological strategy adopted by a Canadian health authority to evaluate its capacity in managing health crises. Notably, this study marks the first instance where a Canadian health authority has created a tool for emergency healthcare management, informed by literature in the field and their direct experience from handling the SARS-CoV-2 pandemic.
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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 | 0.120 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".