The Indirect Effect of Depression between Nightmares and Well‐Being in Lebanese Patients with Schizophrenia
Bibliographic record
Abstract
Background. Because nightmares seem to be associated with depression in schizophrenia, detecting them early in therapeutic practice might be critical to ensuring effective avoidance of the development of depressive symptomatology. This helps promote well‐being and improve the patient’s quality of life and illness prognosis. Therefore, the aim of this study was to examine the indirect effect of depression between nightmares and well‐being in a Lebanese sample of patients with schizophrenia. Method. This monocentric cross‐sectional study, conducted in July 2022, enrolled patients with chronic schizophrenia admitted to the Psychiatric Hospital of the Cross. Data were collected from a total of 148 participants through face‐to‐face interviews. The questionnaire included a nightmares measure, PSYRATS, Calgary depression scale for schizophrenia, PTSD checklist for DSM‐5, the digit span subset, and WHO‐5Well‐Being Index. Results. The presence of nightmares was significantly associated with more depression, whereas higher depression was significantly associated with lower well‐being. It is noteworthy that the presence of nightmares was not directly associated with well‐being. Conclusion. Nightmares lead indirectly to lower well‐being in schizophrenia patients, with depression serving as a mediating factor in this association. This suggests that interventions aiming at improving dream content may also have a beneficial effect in reducing depression in schizophrenia leading therefore to better well‐being of the patients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".