Development and Psychometric Relationship between Occupational Prestige and Social Health of Nursing Staff: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: One of the features that any profession possesses is that it occupies a social status relative to other professions. The present paper contributes to the discussion on the relationship between nursing occupational prestige and social health. METHOD: This study was a cross-sectional research that was conducted in 2015. The study population consisted of 200 staff nurses working in all hospitals located in Hamedan, Iran. A convenience sampling was employed to select 200 nurses who were asked respond to the 25item Keyes’s Social Health Questionnaire and the16-item Nursing Occupational Prestige Questionnaire. Higher score reflected better social health and occupational prestige. The quantitative analysis of this study was carried out using the Statistical Package for Social Science (SPSS). RESULTS: The results showed that the nurses’ mean score of social health and nursing occupational prestige were respectively 43.88±9.18 and occupational prestige44.83±25.14. Social health was significantly related to occupational prestige in the nurses (r=0.15, P=0.04). 66.5% of the nurses were reported to have average occupational prestige and 75.5% of them were believed to have average social health. There was a statistically significant difference in scores of the occupational prestige among the nursing post (P=0.01), job satisfaction (P<0.001) and job interest (P=0.007). CONCLUSION: Nurses’ social health deserves special attention. Effective social health promotion strategies should be considered for promoting their social health particularly in occupational prestige.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".