The Effect of Perceived Organizational Support of Nurses on Their Resilience: A Cross-Sectional Study From Turkey
Bibliographic record
Abstract
BACKGROUND: Throughout and following the ongoing Coronavirus outbreak, there is an urgent need to focus on organizational support strategies aimed at improving the resilience of nurses. PURPOSE: the relationship between the nurses' perceived organizational support and their resilience levels, and to reveal the characteristics that make a significant difference. METHODS: The data of this descriptive and cross-sectional study were collected from 722 nurses in February 2021 using the web-based survey method. The study followed the STROBE guideline. The data collection tools included the Introductory Information Form, the Survey of Perceived Organizational Support, and the Psychological Resilience Scale. RESULTS: Nurses were concluded to have perceived a moderate level of organizational support and their psychological resilience were found to be higher than average. A positive relationship was determined between the organizational support perceived by nurses and their psychological resilience. The gender, position/title of nurses, their work experience in COVID-19 treatment services, and having been infected with the COVID-19 virus were found to affect their perception of organizational support and resilience. CONCLUSION: Organizational support perceived by nurses significantly affects their resilience. Resilience programs should, in particular, prioritize permanent clinical nurses who are in the risk group in terms of resilience, female nurses, nurses who had been infected with the COVID 19 virus, and nurses who have been assigned to COVID-19 treatment wards.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".