Visualising the voices of nursing: a co-designed video capturing the lived experiences of nurses in Northern Ireland during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background Nurses were at the forefront of managing the COVID-19 pandemic. In response, the Royal College of Nursing in Northern Ireland commissioned a longitudinal qualitative survey using the Cognitive Edge SenseMaker ® tool to capture nurses’ experiences of delivering care from April 2020 to March 2021. Aim To explore the effect of a co-designed video based on the findings of SenseMaker ® , of the lived experience of nurses in Northern Ireland during the 2020/2021 global pandemic. Method Quotes were selected from the SenseMaker ® report of nurses’ ( n = 676) which conveyed the experiences of nurses during COVID-19. Three co-design workshops were conducted. The first covering the plan for extraction of data from the SenseMaker ® report, the second content development and script writing and the third covering feedback and revisions. The video was filmed and edited in the Drama Studies, School of Arts, English and Languages, Queen’s University Belfast. The live launch event took place in the Royal College of Nursing conference venue on 8th February 2024. Data to gauge the effect of the video were gathered via audience participation, MS Teams Version 1.7. chat participation, Mentimeter poll and MS forms survey. The link for the video and survey was accessible via the School of Nursing and Midwifery, Queens University Belfast website after the launch event. The findings were analysed using systematic text condensation using NVivo version 1.6. The study was approved by the Faculty of Medicine, Health and Life Sciences research committee at Queen’s University Belfast following peer review (REC Reference: MHLS 23_100). Results Twenty-eight participants completed the survey; 30 participants attended the live event. Overall, 93% ( n = 26) of participants confirmed that the video-based monologues effectively conveyed the emotional perspective and lived experiences of nurses during the COVID-19 pandemic. Four themes emerged - personal reflection and emotional effect, connection and solidarity amidst disappointment, moral injury and resentment, lessons learned and the need for change. Conclusion Wider dissemination of the co-created video-based narratives, with focus on advocacy to policy makers, is needed to prioritise the emotional well-being of nurses and other professionals. There is potential in using video-based monologies to facilitate positive change and better support for professionals, including nursing students in future crises. Further research is needed to assess the broader effect of such healthcare-related research methodologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".