General Practice and Pandemic Influenza: A Framework for Planning and Comparison of Plans in Five Countries
Bibliographic record
Abstract
BACKGROUND: Although primary health care, and in particular, general practice will be at the frontline in the response to pandemic influenza, there are no frameworks to guide systematic planning for this task or to appraise available plans for their relevance to general practice. We aimed to develop a framework that will facilitate planning for general practice, and used it to appraise pandemic plans from Australia, England, USA, New Zealand and Canada. METHODOLOGY/PRINCIPAL FINDINGS: We adapted the Haddon matrix to develop the framework, populating its cells through a multi-method study that incorporated the peer-reviewed and grey literature, interviews with general practitioners, practice nurses and senior decision-makers, and desktop simulation exercises. We used the framework to analyse 89 publicly-available jurisdictional plans at similar managerial levels in the five countries. The framework identifies four functional domains: clinical care for influenza and other needs, public health responsibilities, the internal environment and the macro-environment of general practice. No plan addressed all four domains. Most plans either ignored or were sketchy about non-influenza clinical needs, and about the contribution of general practice to public health beyond surveillance. Collaborations between general practices were addressed in few plans, and inter-relationships with the broader health system, even less frequently. CONCLUSIONS: This is the first study to provide a framework to guide general practice planning for pandemic influenza. The framework helped identify critical shortcomings in available plans. Engaging general practice effectively in planning is challenging, particularly where governance structures for primary health care are weak. We identify implications for practice and for research.
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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 | 0.038 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".