Experiences of healthcare providers from a working week during the first wave of the COVID-19 outbreak.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE WORK: The delivery of care to patients with COVID-19 enhanced many psychological issues among healthcare workers (HCWs), exacerbating the risk of burnout and compromising the efficacy and quality of services provided to patients. In this context, the peculiarities regarding professional roles in delivering care to patients with COVID-19 might reflect daily lived experiences that could impact psychological outcomes in specific professional groups. However, daily lived experiences considering different groups of HCWs have been poorly investigated, especially with a longitudinal qualitative study. Accordingly, our study aims firstly to longitudinally explore perceptions and experiences of HCWs about their daily working life during the initial COVID-19 outbreak, highlighting the specific lived experiences of physicians, nurses, radiology technicians, and healthcare assistants. METHODS: A longitudinal qualitative content analysis was conducted to analyse the comments and quotations made on a daily diary lasting seven days by physicians, nurses, radiology technicians, and healthcare assistants during the first wave of the COVID-19 outbreak. According to Elo and Kyngäs recommendation, the data analysis process was developed in three main phases: preparation, organising, and reporting. RESULTS: Four main generic categories emerged by data analysis: 'Clinical practice in COVID-19 patients'; 'The importance of relationship'; 'Navigating by sight'; and 'Good always pays off'. Several differences emerged from the sentences of the HCWs, which require further investigation. CONCLUSIONS: Understanding the profession-specific experiences of the involved HCWs in facing the challenges of the COVID-19 pandemic is key for boosting reflections, research, and actions to adequately support each professional group.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".