Factors associated with high use of general practitioner and psychiatrist services among patients attending an addiction rehabilitation center
Bibliographic record
Abstract
BACKGROUND: This study aimed to 1) identify the characteristics of individuals with substance use disorders (SUDs) who make high use of services provided by general practitioners (GP) and psychiatrists while receiving services concurrently from an addiction rehabilitation center (ARC), and 2) to compare high service users to moderate and low service users. METHODS: Data were compiled for 4,407 individuals with SUDs who were receiving services from an ARC in 2004. The data came from the merging of four databases: the ARC data registry (January 1(st), 2004-December 31, 2004), the Quebec Health Insurance Board database (March 31, 2003-April 1st, 2005), the Quebec provincial database for hospitalizations (March 31, 2003-April 1st, 2005), and the Quebec National Institute of Public Health database (2004). Independent variables were grouped according to the Andersen Behavioral Model of Health Services Use: predisposing, enabling and need factors. Generalized estimating equations analyses were performed to assess the influence of individual and neighborhood-level characteristics on high use of services outside the ARC provided by GPs and psychiatrists. Benjamini-Hochberg's procedure was applied to correct for multiple comparisons. RESULTS: About 97 % of individuals attending the ARC consulted a GP or a psychiatrist during the two-year study period, for a mean of 1.5 consultations per month. Findings revealed that 5 % of the sample made 26 % of all consultations over the two years, and they were defined as high users. No single predisposing factor was associated with high use. One enabling factor significantly increased the risk of being a high user of services from general practitioners and psychiatrists: receiving services at the ARC for three years prior to 2004. Four needs factors, all related to mental health diagnoses (schizophrenia, mood disorder, anxiety disorder, personality disorder), predicted high use of general practitioner and psychiatrist services. CONCLUSIONS: This study found that nearly all individuals with SUDs receiving services from an ARC were users of health services from GPs and psychiatrists outside the ARC. High users most probably accessed them in inpatient settings. No previous study has compared high service users with low and moderate users among individuals with SUDs. Considering that ARCs are treating individuals with complex needs, some of whom make high use of medical professionals, both ARCs and their clients could benefit from increased collaboration and integration between the addictions and mental healthcare sectors.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".