A PhotoVoice Exploration of Pharmacists’ Lived Experiences Working During the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: Pharmacists’ pivotal role during the COVID-19 pandemic has been widely recognized, as they adapted to continue to provide an even higher level of care to their patients. We sought to gain deeper understanding of frontline pharmacists’ lived experiences of the COVID-19 pandemic and its impact on their roles and professional identity (what they do and what it means to them). Method: Photovoice was used, a visual research method that uses participant-generated photographs to articulate their experiences, and semi-structured interviews. This approach allowed us to explore the subjectivity of professional identity from the pharmacists’ lived experiences. Participants were asked to provide 3-5 photos that reflected on how they see themselves as a pharmacist and/or represents what they do as a pharmacist. The semi-structured interview guide asked open-ended questions about their photos, included a photo-elicitation exercise, and additional questions based on a recent scoping review. We recruited frontline community pharmacists who provided direct patient care during the COVID-19 pandemic in Alberta, Canada through social media and relevant pharmacy organizations. Data analysis incorporated content, thematic and visual analysis and was facilitated using NVivo software. Ethics approval was obtained from the University of Alberta ethics board. Results: Five primary themes emerged from the photographs and interviews: (1) autonomy, (2) clinical courage, (3) leadership, (4) safety, and (5) value and support. The photographs identified symbols participants associated with their lived experiences (e.g., worn shoes illustrate the relentless pace of pharmacists, a messy bed representing work-life balance out of control). Conclusion: This study identified that pharmacists’ felt the pandemic made them visible to the public and made them feel valued as a trusted resource and a safe-haven for ongoing healthcare. Additionally, it was highlighted how participants demonstrated clinical courage and led their communities by adapting their roles and using their autonomy to fulfil community needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".