Photovoice Exploration of Frontline Nurses’ Experiences During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: The current COVID-19 global pandemic has had a profound impact on the health care system and on the physical and psychological well-being of nurses. Previous pandemics have led to nurses leaving the profession. Therefore, it is important that we hear the voices of nurses who experienced the pandemic on the frontlines to influence future planning and policy development. PURPOSE: The purpose of this study was to explore frontline nurses' experiences during the COVID-19 pandemic through photos, narratives, and group discussions. METHODS: Twelve nurses in two groups shared their lived experiences through Photovoice, a participatory action approach. Photos and narratives were collected over five weeks per group. One group at the beginning of the pandemic and the other group six months later. Focus group discussions were held following each group. RESULTS: Five themes emerged from the photovoice data: (1) The work of nursing; (2) Miscommunication; (3) Fatigue; (4) Resilience; and (5) Hope for the future. Various subthemes were noted within each theme to delineate the lived experience of frontlines nurses working in the COVID-19 pandemic. CONCLUSIONS: The voices of nurses and their experiences on the frontlines of the COVID-19 pandemic need to be considered in pandemic planning and integrated into health care policy, guidelines, and structural changes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".