Effective decision-making in public health organizations: reference to the COVID-19 pandemic
Bibliographic record
Abstract
PURPOSE: The role of public health organizations during the COVID-19 pandemic was crucial. These groups acted to slow the spread of infection through the implementation of initiatives, policies, research and more. However, the rapidly changing and uncertain climate of the pandemic resulted in suboptimal processes and decision-making within these organizations. These already complex organizations and networks of people became even more nuanced. Thus, organizational decision-making processes must be improved upon based on previous experiences and lessons learnt. With minimal peer-reviewed literature available, resources for effective organizational decision-making in these organizations are scarce. This served as the impetus for this review. DESIGN/METHODOLOGY/APPROACH: To conduct this literature review, both peer-reviewed and grey literature were incorporated to better understand effective organizational decision-making practices for public health organizations. Recommendations found in the literature review were identified, coded and themed to provide a novel decision-making framework to be used by public health executives. FINDINGS: Nine key themes of effective organizational decision-making were identified, including utilize decision-making tools, define the problem and acknowledge an imminent decision, establish decision rights, outline a clear escalation path, create a supportive organizational culture, set decision objectives and goals, and evaluate decision alternatives. These findings in conjunction with existing decision-making models were used to create a seven-step effective decision-making framework for public health organizations. ORIGINALITY/VALUE: The review and analysis of effective organizational decision-making practices is instructive. Public health executives and decision-makers should incorporate the themes identified and employ the proposed decision-making framework to encourage improved decision-making practices.
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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 | 0.092 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".