WORKPLACE SECURITY; FEMALE NURSES FACTORS PREDICTING PERCEIVED IN PUBLIC SECTOR HOSPITALS IN LAHORE
Bibliographic record
Abstract
Introduction: This study is about female nursing staff whose mission is toprovide professional care to ailing patients, but who are also vulnerable to bullying, harassment,and even assault while they perform their duties. Objectives: The study attempts to describethe level of perceived workplace security among the nurses and investigate relationshipbetween theoretically identified predictors and the perceived workplace security of nursesworking in public hospitals in Lahore city. Study Design: Factors such as civility in co-workerbehavior, clarity of norms of communication with co-workers, integrity and trust of the leaders,awareness of law and legal procedure related to harassment at work place and physicalsettings characteristics—including transparency and privacy at the workplace—were modeledthrough a cross-sectional research design as contributing towards workplace security of femalenurses. Study Period: Oct 2015 to May, 2016. Methods: Multi-stage probability sampling wasused to collect data from 317 respondents working in five public sector hospitals in Lahorecity during first quarter of 2016. Zero-order correlation and multiple regression methods wereused to analyze the data. Findings: Results showed that civility (B=.071, p<.000) in co-workerbehavior, integrity (B=.185, p<.000) and trust of leaders (B=.059, p<.000) is the most potentfactor influencing perceived workplace security of female nurses. Moreover, clear norms ofcommunication (B=.169, p<.000) and privacy (B=.133, p<.000) at workplace also significantlyinfluence their perceived workplace security. Transparency (B=.017, p>.05) in physical settingsand awareness (B=-.014, p>.05)of law and legal procedures were found to be insignificant.Conclusion: It was concluded that setting clear work norms and leadership development inthe areas of integrity and trust can play a substantial role in improving perception of workplacesecurity in female nursing staff. Awareness of laws regarding harassment among nurses needto be increased through formal and concerted effort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".