Exploring Volunteer Pharmacists’ Experiences in Responding to 2023 Türkiye Earthquakes: A Qualitative Phenomenological Study
Bibliographic record
Abstract
OBJECTIVE: Pharmacists are vital in disaster response efforts, dispensing essential medications, managing pharmacy services, consulting, and educating survivors regarding their medications. Their contributions, however, are often underrepresented in scientific literature. This study aimed to explore the experiences of pharmacists who provided pharmacy services to meet the pharmaceutical needs of the survivors after 2 major earthquakes in Türkiye in 2023. METHODS: This study adopted a phenomenological approach. Data were collected using semi-structured interviews. Purposive sampling was used to invite pharmacists who provided pharmacy services to survivors. Interview transcripts were analyzed following an inductive, reflexive thematic analysis. RESULTS: In total, 15 pharmacists were interviewed. Four main overarching themes "response to the earthquake," "preparedness for the earthquake," "experiences during service delivery," and "mental and physical experiences" were developed. CONCLUSIONS: From participants' experiences, it is essential to expand the clinical responsibilities of pharmacists and train them in providing wound care, administering immunization, and prescribing. Pharmacists should be integrated as essential members of disaster health teams. International health organizations, nongovernmental organizations, and governments are encouraged to work collaboratively and develop disaster management plans including pharmacists in early responders. This might help mitigate the deficiencies and overcome challenges in health-care systems to provide effective patient-centered care by health professionals and respond effectively to disasters.
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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 | 0.004 | 0.007 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".