Do disasters predict international pharmacy legislation?
Bibliographic record
Abstract
Objective The aim of this study was to explore whether a relationship exists between the number of disasters a jurisdiction has experienced and the presence of disaster-specific pharmacy legislation. Methods Pharmacy legislation specific to disasters was reviewed for five countries: Australia, Canada, UK, US and New Zealand. A binary logistic regression test using a generalised estimating equation was used to examine the association between the number of disasters experienced by a state, province, territory or country and whether they had disaster-specific pharmacy legislation. Results Three of six models were statistically significant, suggesting that the odds of a jurisdiction having disaster-specific pharmacy legislation increased as the number of disasters increased for the period 2007-17 and 2013-17. There was an association between the everyday emergency supply legislation and the presence of the extended disaster-specific emergency supply legislation . Conclusions It is evident from this review that there are inconsistencies as to the level of assistance pharmacists can provide during times of crisis depending on their jurisdiction and location of practice. It is not a question of whether pharmacists have the skills and capabilities to assist, but rather what legislative barriers are preventing them from being able to contribute further to the disaster healthcare team. What is known about the topic? The contributing factors to disaster-specific pharmacy legislation has not previously been explored in Australia. It can be postulated that the number of disasters experienced by a jurisdiction increases the likelihood of governments introducing disaster-specific pharmacy legislation based on other countries. What does this paper add? This study compared five countries and their pharmacy legislation specific to disasters. It identified that as the number of disasters increases, the odds of a jurisdiction having disaster-specific emergency supply or disaster relocation or mobile pharmacy legislation increases. However, this is likely to be only one of many factors affecting the political decisions of when and what legislation is passed in relation to pharmacists' roles in disasters. What are the implications for practitioners? Pharmacists are well situated in the community to be of assistance during disasters. However, their ability to help patients with chronic disease management or providing necessary vaccinations in disasters is limited by the legislation in their jurisdiction. Releasing pharmacists' full potential in disasters could alleviate the burden of low-acuity patients on other healthcare services. This could subsequently free up other healthcare professionals to treat high-acuity patients and emergencies.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".