Psychometric Properties of the Multidimensional Scale of Perceived Social Support in Greek Nurses
Bibliographic record
Abstract
BACKGROUND: Psychosocial well-being in the workplace may increase retention of oncology nurses, while a lack of social support has been a predictor of occupational stress in nurses. PURPOSE: To further explore this phenomenon by examining the psychometric properties of the Multidimensional Scale of Perceived Social Support in Greek nurses working in oncology and mental health settings. METHODS: A cross-sectional design was used. The sample consisted of 150 nurses (70 oncology nurses and 80 mental health nurses) from the area of greater Athens who joined the study. The scale was translated to Greek using the "forward-backward" procedure. The Multidimensional Scale of Perceived Social Support's internal consistency reliability (Cronbach's α), stability (intraclass correlation coefficient), factor structure (factor analysis), and convergent validity (correlation with Ways of Coping Questionnaire) were examined. RESULTS: A three-factor (significant others, family, and friends) model was confirmed. The subscales representing the three factors demonstrated excellent internal consistency reliability (Cronbach's αs > .90) and stability intraclass correlation coefficient (>.90). CONCLUSIONS: The measure is reliable and valid, and it can be used to assess nurses' social support; the results of such an assessment could be helpful when selecting strategies for assisting nurses.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".