An Exploration of the Impacts of the 2019 Floods in Townsville, Australia on Community Pharmacy Operations
Bibliographic record
Abstract
Between January 29 and February 11, 2019, the Townsville region in Australia experienced a major flooding event. This study explored impacts on affected community pharmacies. Semi-structured phone interviews were conducted with six pharmacists who worked in affected Townsville community pharmacies during this flood. De-identified transcript data were analyzed using reflexive thematic analysis. The thematic analysis yielded six themes - "financial impact on pharmacy owners," "engagement with Local Disaster Coordination Center (LDCC) important," "workload pressures," "preparedness," "medication supply impacts," and "communication and collaboration." Financial impacts to owners included loss of property (two pharmacies were completely flooded), purchase or hire costs of generators when power was lost, and loss of revenue from complete or early closure of pharmacies and when patients could not pay or did not have a prescription and did not return to the pharmacy after the event. Engagement with the LDCC assisted pharmacy responsiveness. Medication supply issues were experienced by patients whose houses had flooded, or who had left their prescriptions with pharmacies that had flooded. Opioid Replacement Therapy (ORT) program patients were also impacted due to communication difficulties between them, their clinics, and their pharmacies. Increased customer numbers by those whose regular pharmacy was closed, reduced staff numbers, and austere working conditions increased workload pressures. Pharmacists collaborated to consolidate resources with those whose pharmacy had closed, working in pharmacies that were open. This research highlights a critical need for improved flood preparedness among Townsville pharmacists. Regardless, they collaborated to ensure there were minimal critical medication delays.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".