Predictors of Mental Health Service Utilization by People Using Resources for Homeless People in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study used Pescosolido's network episode model to examine mental health service utilization among impoverished people accessing resources for the homeless in Canada's universal health care setting.The sample consisted of 439 people who met DSM-IV criteria for affective or psychotic disorders who were assessed as part of a larger study of resources for homeless or impoverished people in Montreal and Quebec City. Interviews were organized into the framework of four network episode model concepts: sociodemographic characteristics, illness characteristics, illness history, and social network. These blocks of variables were then analyzed in terms of their accuracy in predicting mental health service utilization.Eighty-four percent of the sample were male, the mean+/-SD age was 41+/-12 years, and 36% were homeless at the time of the interview, but nearly half (48%) of the population had been homeless previously. The research shows that each network episode model concept except illness history significantly predicted utilization of mental health services. Female gender, youth, never being homeless (sociodemographic characteristics), presence of antisocial personality disorders within the preceding year, past or current alcohol-related disorders (illness characteristics), hospitalization before the preceding year (illness history), and a larger social support network were related to utilization of mental health services.In the absence of economic barriers to health care, there are other significant barriers to the use of mental health services for people who live in poverty. A better understanding of these factors will help in meeting the service needs of impoverished mentally ill people.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it