Worldwide Impact of COVID-19 on Frontline Pharmacists’ Roles and Services: INSPIRE International Questionnaire
Bibliographic record
Abstract
Introduction: Pharmacists have been recognized as essential healthcare professionals during the COVID-19 pandemic. However, evidence of the challenges that were faced by the profession and the way pharmacists adapted their roles throughout the pandemic are largely unknown. This study aimed to describe the impact of COVID-19 on pharmacy practice around the world. Method: A cross-sectional online questionnaire with pharmacists who provided direct patient care during the pandemic. Pharmacists were recruited through social media with assistance from national/international pharmacy organizations. The questionnaire was divided into three sections; 1) demographics, 2) pharmacists’ roles/services during the pandemic, and 3) practice challenges. The questionnaire was adapted from the established, piloted, and published INSPIRE Canadian Survey. The data were analyzed using SPSS 28. Descriptive statistics were used to report frequencies and percentages. Results: A total of 505 pharmacists practicing in 25 countries consented and completed the questionnaire. Only 26.4% (132/500) of participants were engaged with local disaster and public health agencies during the pandemic to coordinate pandemic response. The most common role that pharmacists undertook was responding to drug information requests (89.4%, 448/501), followed by allaying patients' fears/anxieties about COVID-19 (82.7%, 413/499), educating the public on reducing the spread of COVID-19 (81.3%, 409/503), and addressing misinformation on COVID-19 treatments/vaccinations (79.1%, 397/502). The most common services provided by pharmacists were performing medication reviews (78.5%, 391/498) and managing and/or monitoring patients’ chronic diseases (72.3%, 362/501). Almost half of the participants reported administering COVID-19 vaccines (44.9%, 225/501). The most common challenge that pharmacists encountered was increased stress level (82.2%, 415/505), followed by medication shortages (72.3%, 360/505). Conclusion: Despite the unprecedented nature of the COVID-19 pandemic and the various challenges associated with it, pharmacists around the world adapted their roles and services to continue to meet the needs of their patients and be their safe-haven for ongoing care.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".