Diagnosis of mental disorders in caregivers of patients with bipolar disorder: a gap in the scientific literature
Bibliographic record
Abstract
OBJECTIVE: Caregivers appear to experience mental health implications related to their role in supporting patients with bipolar disorder (BD). This study aimed to review the literature assessing the presence of not only psychiatric symptoms but also mental disorders and personality disorders in caregivers of patients with BD. METHODS: This is a narrative review. Articles were searched in some online databases, including PubMed, Embase, Web of Science, PsychINFO, Lilacs, and SciELO up to June 2024. Inclusion criteria comprised articles in any language, focusing on primary caregivers of patients with BD, and quantitative studies evaluating the presence of mental disorder, personality disorder, or psychiatric symptoms in this caregiver group. RESULTS: The review encompassed 15 articles. Only two studies utilized appropriate diagnostic instruments for assessing mental disorder, and no article evaluated personality disorder. Prevalence of caregiver's psychiatric symptoms and mental disorder was found to be higher compared to the general population. CONCLUSION: The lack of studies employing diagnostic assessment tools for mental disorder in these individuals may impede timely access to treatment, affecting both the caregiver's health and the clinical course of patients with BD. This study provides an updated overview of research on caregivers of patients with BD, despite the limitation of not being a systematic review. Further studies employing diagnostic assessments on caregivers are essential to gain deeper insights into this critical topic.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".