Determining the Influence of Life Change Events on the Mental Health Nurses; A Case of Saudi Arabia
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Few researches are found concerning the relationship of life-altering events and psychological health among mental health nurses in Saudi Arabia. Thereby, the study examines the influence of life-altering events on Saudi mental health of nurses. METHODS: A descriptive correlational research design was used where mental health nurses from three different hospitals were recruited using a random sampling method. Data was collected using Qandil’s Arabic Modified Life Events Questionnaire. Pearson correlation evaluated the relationship between variables. RESULTS: Major change in eating habits was responsible for expressing both depression and stress. Inclusion of new members, leaving loved ones due to several causes, spouses’ death, and substantial changes in the family members’ health status are all significantly related to depression. Change occurred in the parents’ marital status due to divorce or death also became the cause of depression. This is also same when person felt burden in taking care of the sick family member. Going on vacations and short trips, change in the meetings of family and social activities may help in relieving depression. CONCLUSION: The findings show that big personal achievements lead to negative relationship with depression. Stress-related factors are also a personal problem, which must be intervened with the enhancement of the working conditions.
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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 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".