Healthcare Professionals’ Resilience During the COVID-19 and Organizational Factors That Improve Individual Resilience: A Mixed-Method Study
Bibliographic record
Abstract
Healthcare workers are a susceptible population to be psychologically affected during health crises, such as the recent COVID-19 pandemic. Resilience has been pointed out in the literature as a possible protective factor against psychological distress in crisis situations. This can be influenced by internal and external factors, such as individual characteristics and organizational factors. Thus, this study aims to characterize the overall resilience levels among healthcare professionals in Portugal and to understand the perspectives of this healthcare workers regarding organizational factors that improve individual resilience. This is a mixed-method study: a first quantitative study using a cross-sectional design to administer the Resilience Scale for Adults (RSA) to 271 healthcare professionals (Mage 33.90, SD = 9.59 years, 90.80% female), followed by a qualitative study through 10 in-depth interviews. The mean score for the total RSA was 178.17 (SD = 22.44) out of a total of 231. Qualitative analysis showed 4 major themes on factors that enhance resilience: "Professional's Training," "Support and Wellbeing Measures," "Reorganization of Services" and "Professional Acknowledgment." The findings may contribute to the development of targeted interventions and support systems to enhance resilience and well-being among healthcare workers.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".