The Incidence of Depression among female patients with Schizophrenia Hospitalized in the Clinical Hospital of Psychiatry, Chișinău, R. of Moldova
Bibliographic record
Abstract
Introduction In schizophrenia, depressive and negative symptoms often overlap, complicating the diagnosis. Symptoms such as lack of energy, anhedonia and association make the distinction difficult. A subjective state of sadness may indicate depression, while affective flattening is characteristic of schizophrenia. Other symptoms relevant to diagnosing depression include hopelessness, negative self-assessment, guilt, anxiety, and self-destructive thoughts. Objectives This study, conducted over 6 months, aims to establish both the rate and the total number of patients with schizophrenia, suffering from depression to provide a better understanding of the clinical particularities of this type of comorbidity. Methods The research provided for a cross-sectional study to assess the incidence of depression among patients with schizophrenia hospitalized in the Clinical Psychiatric Hospital of Chisinau during the reference period. The Calgary depression scale for schizophrenia (CDSS) has been used as an evaluation tool to highlight the presence of depressive symptoms, while PANSS (negative symptoms) has been used, to measure their severity in schizophrenia. The data collection process involved structured questionnaires, semi-structured interviews with patients and clinical history analysis to obtain additional information. Results The study included 155 women diagnosed with schizophrenia, aged between 18 and 55. Of these, 28.39% had more than two admissions during the year. Most of the patients included in the study (73.55%) suffered from F20.0, the rates of other forms of schizophrenia being: F20.1 (1.29%); F20.2 (14.84%); F20.3 (9.68%) and F20.9 (0.65%). According to the questionnaire applied to patients with different types of schizophrenia, it was observed that, 40.65% achieved a total score of more than 6 points on the CDSS scale (clinically significant depression) and 34.92% required repeated hospitalisation in the same year. Of those repeatedly admitted, 74.6% suffered from F20.0; 14.29% (F20.2) and 11.11% (F20.3), which probably could be a confirmation of the increased severity of the patient’s condition in case of comorbidity – schizophrenia-depression. Conclusions It can be assumed that the comorbidity of schizophrenia-depression negatively influences the social recovery process, but also the quality of life of patients; increases the risk of relapse. Patients with depression often experience higher rates of hospitalization because depressive symptoms can lead to emotional instability, cognitive impairments, undermining interpersonal relationships, social networking and reintegration. It is essential that mental health professionals identify and treat depression in patients with schizophrenia in order to increase their quality of life. Disclosure of Interest None Declared
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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 | 0.001 |
| 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) | 0.002 | 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".