Factors Associated With Opiate Use Among Psychiatric Inpatients: A Population-Based Study of Hospital Admissions in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Use of opiates, including synthetic opioids, is associated with a number of negative consequences, including increased risk of opioid use disorders and other mental health conditions. However, studies are limited in examining patterns of opiate use among persons in inpatient psychiatry, particularly those that consider the relationship between pain and opiate use. OBJECTIVE: This study examined the prevalence in the prior 12 months to admission and patterns of opiate use and pain in a population-based study of persons admitted to inpatient psychiatry in Ontario, Canada. METHODS: We conducted retrospective cross-sectional study of 165 434 persons admitted to inpatient psychiatry between January 1, 2006 and December 31, 2017. Using data from the Resident Assessment Instrument for Mental Health, we examined prevalence and factors associated with opiate use in the prior 12 months by a number of patient characteristics, including demographics, mental and physical health status, concurrent substance use, pain severity and frequency, and health region of residence. RESULTS: = 0.91), including being of younger age, use of other substances, greater frequency and severity of pain, and health region of residence. CONCLUSION: The strong relationship between pain and opiate use in this population, and the regional variation in this pattern, supports the need for integrated care for mental illness and substance use, and therapeutic approaches to pain management that reduce risks of problems associated with substance use for persons with mental health conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".