Sleep Quality and Correlated Factors among Retired Nurses in the North-east of Iran
Bibliographic record
Abstract
People in occupations with an extreme amount of stress, such as nursing, suffer from poor physical and mental health after retirement. This study was aimed at evaluating sleep quality scores and their correlates among retired nurses in the north-east of Iran. This cross-sectional study was conducted on 302 retired nurses in public hospitals of north-east Iran between April and May 2018. The data were collected using the Persian Version of Pittsburgh Sleep Quality Index (PSQI), a valid and reliable scale to evaluate sleep quality among Iranian people through phone calls. The mean age of subjects was 56.6 ±4.6 and 66.9% were female. Altogether 82.7% of retired nurses had poor sleep quality. According to multiple regression analysis, males had a significantly better overall sleep quality compared to females. Participants with evening and rotational shifts had a significantly lower sleep quality as compared to those working in the morning shift. Subjects suffering from musculoskeletal diseases, cardiovascular diseases (and a combination) had substantially poorer sleep quality as compared to those with no comorbidity. Findings suggest that Iranian retired nurses do not have good sleep quality. Health systems and their managers play an important role in preparing nurses for retirement. They can reduce post-retirement complications by designing a normal employee work schedule, increasing the nursing workforce when needed, and preventing overwork and long?term overtime hours.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".