Perceived unmet substance use and mental health care needs of acute care patients who use drugs: A cross‐sectional analysis using the Behavioral Model for Vulnerable Populations
Bibliographic record
Abstract
INTRODUCTION: The perceived unmet service needs of acute care-seeking people who use illegal drugs (PWUD) have been poorly documented, despite evidence of frequent hospital utilisation. This study applies the Behavioral Model for Vulnerable Populations to investigate correlates of unmet service needs in this subpopulation. METHODS: Survey data from 285 PWUD at three urban Canadian acute care centres were examined. The survey included the Perceived Need for Care Questionnaire, which measured service seeking and care satisfaction for mental health and substance use concerns across seven types of services, as well as barriers to having care needs met. The Behavioral Model for Vulnerable Populations was applied in hierarchical setwise logistic regression to examine associations between high unmet service need and socio-structural predictors (i.e. predisposing, enabling and need factors). RESULTS: Almost half (46%) of participants reported a high level of unmet service need, despite seeking services during the past year. Participants reporting recent criminal activity, adverse childhood experiences, transitory sleeping, having no community support worker, and meeting screening criteria for depression were more likely to report a high level of unmet service needs. Structural barriers to care (57%) were more commonly reported than motivational barriers (43%). DISCUSSION AND CONCLUSIONS: Acute care-seeking PWUD experience high rates of unmet service needs for their mental health and substance use problems. Strategies that can help overcome structural barriers to care are necessary to help address the service needs of this population.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".