Perceived loss among people living with mental disorders: Validation of the personal loss from mental illness scale
Bibliographic record
Abstract
OBJECTIVE: The development of mental illness often leads to pervasive losses in different areas of people's lives. However, previous research has tended to focus on the loss experienced by families while the examination of the loss experienced by individuals who are themselves coping with mental illness has been neglected. The present study tested the factor structure of the Hungarian version of the Personal Loss from Mental Illness (PLMI) scale, and analyzed its associations with age, gender, previous hospitalizations, marital status, loneliness, grief, and quality of life. METHODS: Mentally ill patients (N = 200) with different diagnoses were recruited from a mental health center in Hungary, and completed self-report questionnaires. Confirmatory factor analysis (CFA) with covariates was conducted. RESULTS: CFA analyses rejected the previous four-factor structure and suggested a single factor structure to be superior. Higher loss perception was predicted by higher loneliness, grief, and lower quality of life. Patients with mood disorders reported higher loss as compared to patients with other psychiatric diagnoses. CONCLUSIONS: The present study stresses the magnitude of loss and raises the need to examine further the role of loss in coping and recovery. Asking patients about their feelings in clinical practice is of high importance.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".