Predictors of Frequent Recourse to Health Professionals by People with Severe Mental Disorders
Bibliographic record
Abstract
OBJECTIVE: Based on Andersen's behavioural model, our study sought to determine predictors and blocks of factors that could explain why people with severe mental disorders (SMDs) more often seek the services of health professionals. METHODS: This longitudinal study involved 292 users with SMDs located in Le Sud-Ouest, the southwest borough of Montreal. Data were collected from participants' medical records and through 7 questionnaires. Using Andersen's Behavioral Model of Health Services Use, independent variables were divided into 3 classes-predisposing factors, enabling factors, and need factors-and were introduced in this order in a hierarchical logistic model. RESULTS: Among 292 users, 110 (37.7%) were frequent users who consulted about one health professional every 3 days. Participants who were more likely to call on health professionals were single and older, depended on welfare as their main source of income, lived in supervised housing, suffered from schizophrenia, schizophrenia spectrum disorders, and adjustment disorders, and, marginally, exhibited multiple mental disorders. CONCLUSION: Mental health services could promote strategies to overcome the reluctance of younger people to seek professional services. Professionals should pay close attention to subsidiary conditions, such as adjustment disorders, from which people with SMDs may suffer. Interventions to improve the socioeconomic condition of unemployed people with SMDs may help to reduce health care service use among that clientele. Programs such as supported employment should be tailored and enhanced for people receiving welfare to decrease stigmatization and improve job market integration.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".