Factors affecting the resilience of hospital medical staff during the COVID-19 pandemic
Bibliographic record
Abstract
Literature Search, G -Funds CollectionBackground.During the coronavirus disease 2019 (COVID-19) pandemic, most hospitals have faced a heavy load of patients.In this situation, it is very important to consider the resilience and endurance of medical staff, as well as to identify and investigate the relevant factors which can increase their resilience.Objectives.The aim of this study was to identify the factors affecting the resilience of hospital medical staff during the COVID-19 pandemic. Material and methods.The present study is a qualitative study using a semi-structured interview.Participants included doctors, nurses, clinicians and managers working in tertiary referral hospitals during the COVID-19 pandemic.Interviews were conducted as needed until data saturation was reached using the purposive sampling method.a total number of 20 people, including 6 physicians, 2 hospital managers, 7 nurses, 1 radiologist, 3 laboratory technicians and 1 clinical psychologist, were interviewed in 7 educational hospitals of the Kerman Province.Results.after data analysis and coding, 127 initial codes were identified.by reviewing the codes, 127 initial codes were merged by the research team, and 23 codes in 6 main categories, including Personal Factors (7 codes), Family-Related Factors (2 codes), Community-Related Factors (2 codes), Virus-Related Factors (2 codes), Organisational Factors (7 codes) and economical Factors (3 codes) were extracted. Conclusions.Paying attention to the identified factors on the maintenance of medical human resources in the form of the "Surge Capacity Programme" can increase the resilience of medical staff.Such measures pave the way for a better response to other threats similar to the COVID-19 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 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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".