Professional Service Utilisation among Patients with Severe Mental Disorders
Bibliographic record
Abstract
BACKGROUND: Generally, patients with serious mental disorders (SMD) are frequent users of services who generate high care-related costs. Current reforms aim to increase service integration and primary care for improved patient care and health-care efficiency. This article identifies and compares variables associated with the use by patients with SMD of services offered by psychiatrists, case managers, and general practitioners (GPs). It also compares frequent and infrequent service use. METHOD: One hundred forty patients with SMD from five regions in Quebec, Canada, were interviewed on their use of services in the previous year. Patients were also required to complete a questionnaire on needs-assessment. In addition, data were collected from clinical records. Descriptive, bivariate, and multivariate analyses were conducted. RESULTS: Most patients used services from psychiatrists and case managers, but no more than half consulted GPs. Most patients were followed at least by two professionals, chiefly psychiatrists and case managers. Care access, continuity of care, and total help received were the most important variables associated with the different types of professional consultation. These variables were also associated with frequent use of professional service, as compared with infrequent service use. In all, enabling factors rather than need factors were the core predictors of frequency of service utilisation by patients with SMD. CONCLUSION: This study reveals that health care system organisation and professional practice--rather than patient need profiles--are the core predictors of professional consultation by patients with SMD. The homogeneity of our study population, i.e. mainly users with schizophrenia, recently discharged from hospital, may partly account for these results. Our findings also underscored the limited involvement of GPs in this patient population's care. As comorbidity is often associated with serious mental disorders, closer follow-up by GPs is needed. Globally, more effort should be directed at increasing shared-care initiatives, which would enhance coordination among psychiatrists, GPs, and psychosocial teams (including case managers). Finally, there is a need to increase awareness among health care providers, especially GPs, of the level of care required by patients with disabling and serious mental disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".