Perceived unmet need and barriers to care amongst street‐involved people who use illicit drugs
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Research on perceived unmet need for care for mental health and substance use problems focuses on general populations to the detriment of hidden populations. This study describes prevalence and correlates of perceived unmet need for care in a community-based sample of street-involved people who use illicit drugs and identifies barriers to care. DESIGN AND METHODS: A sample of 320 street-involved people who use drugs participated in a structured, interviewer-assisted survey in Edmonton, Canada. The survey included the Perceived Need for Care Questionnaire, which assessed unmet need for care for mental health and substance use problems across seven service types. Logistic regression examined the associations between perceived unmet need, extent of socioeconomic marginalisation and problem severity. Barriers underlying unmet service needs were also examined. RESULTS: Most (82%) participants reported unmet need for one or more services during the past year. Odds of reporting one or more unmet needs were elevated amongst participants reporting substantial housing instability (adjusted odds ratio = 2.37; 95% confidence interval 1.19-4.28) and amongst participants meeting criteria for drug dependence (adjusted odds ratio = 1.22; 95% confidence interval 1.03-1.50), even after adjustment for sociodemographic covariates. Structural, rather than motivational barriers were the most commonly reported reasons underlying unmet service needs. DISCUSSION AND CONCLUSION: Street-involved people who use drugs experience very high rates of perceived unmet need for care for mental health and substance use problems. General population studies on perceived unmet need are insufficient for understanding needs and barriers to care in hidden populations.[Hyshka E, Anderson JT, Wild TC. Perceived unmet need and barriers to care amongst street-involved people who use illicit drugs. Drug Alcohol Rev 2017;36:295-304].
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".