Typology of persons with severe mental disorders
Bibliographic record
Abstract
BACKGROUND: Persons with severe mental disorders (PSMD) form a highly heterogeneous group. Identifying subgroups sharing similar PSMD profiles may help to develop treatment plans and appropriate services for their needs. This study seeks to establish a PSMD typology by looking at individual characteristics and the amount and adequacy of help received. METHODS: The study recruited a sample of 352 persons located in south-western Montreal (Quebec, Canada). Cluster analysis was used to create a PSMD typology. RESULTS: Analysis yielded five clusters: 1. highly functional older women with mood disorders, receiving little help from services; 2. middle-aged men with diverse mental disorders and alcohol abuse, receiving insufficient and inadequate help; 3. middle-aged women with serious needs, mood and personality disorders and suicidal tendencies, living in autonomous apartments, and receiving ample but inadequate help; 4. highly educated younger men with schizophrenia, living in autonomous apartments, and receiving adequate help; and 5. older poorly educated men with schizophrenia, living in supervised apartments, with ample help perceived as adequate. Marked differences were found between men and women, between users diagnosed with schizophrenia and others, and between persons living in supervised or autonomous apartments. CONCLUSION: Our study highlights the existence of parallel subgroups among PSMD related to their socio-demographic status, clinical needs and service-use profiles, which could be used to focus more appropriate interventions. For mental health service planning, it demonstrates the relevance of focusing on individuals showing critical needs who are affected by multiple mental disorders (especially when associated with alcohol abuse), and often find help received as less adequate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".