<scp>COVID</scp>‐19 and Healthcare Workers Burnt out: A Qualitative Study of Burnout Experiences of Healthcare workers in a Secondary Hospital in Ghana
Bibliographic record
Abstract
AIM: The COVID-19 pandemic emerged between 2019 and 2021, and had a profound and far-reaching impact across all sectors of society, significantly altering daily life and working conditions especially for frontline health professionals. This study explored the burnout context that occurred among health workers, predominantly nurses during the peak of the COVID-19 pandemic in a secondary hospital facility in Ghana. DESIGN: Descriptive phenomenological design. METHODS: The study adopted a qualitative approach, anchored on a descriptive phenomenological design to explore the dynamics of burnt-out experienced by 12 healthcare staff at the Kwame Nkrumah University of Science and Technology (KNUST) hospital in Ghana during the COVID-19 pandemic. The (12) healthcare workers, which included six (6) male and six (6) female, were recruited using a convenient sampling technique, guided by a structured interview guide. Adopting a thematic approach, the transcribed data were analysed after data categorisation and manual coding. FINDINGS: Healthcare staff reported a significant increase in workload during the peak of COVID-19 cases, which subsided as case severity decreased over time. Healthcare workers recounted the pandemic's psychological toll, including symptoms of increased stress, worry, and despair exacerbated by severe burnout as a result of the crisis's relentless demands. Despite the obstacles, healthcare workers constantly emphasised their dedication to professionalism, indicating a strong feeling of obligation to maintain patient-centred treatment. A highlight was made in respect of exhaustion and emotional difficulties, though these experiences did not impair the quality of treatment delivered to clients. Providing respectful, empathetic, and equitable care to all patients irrespective of suspected COVID-19 or other diseases remained a central feature of services provided. PATIENT OR PUBLIC CONTRIBUTION: This paper presents findings relevant to the provision of quality client care in moments of extreme stress. It provides insights into the challenges nurses go through to secure their health and provide safety for the public in moments of general public fear and discomfort. The findings in this study invite the public to appreciate and equally empathise with emergency health care providers particularly during periods of pandemic.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".