The Invisible Epidemic: A Spotlight on the Opioid Crisis Among Older Adults
Bibliographic record
Abstract
Abstract Canada is facing an opioid crisis, with more than 3,900 related deaths occurring in 2017. Almost 30% of those deaths were among seniors; older adults also have the highest rates of opioid hospitalization and poisoning. Despite these statistics, little is known about the specifics. A comprehensive scoping review following Arksey and O’Malley’s (2005) framework was conducted to establish the magnitude of the problem, describe treatment approaches alongside prevention, and identify implications for practice, policy and research. Eight major electronic databases in medicine and the social sciences were searched alongside the grey literature, and a stakeholder consultation convened to validate results. A total of 6,814 articles, reports and thesis were identified. Forty-five sources met inclusion criteria, the majority stemming from the United States. Most were literature reviews, cohort and cross-sectional studies, with almost half also taking a gendered approach. Four predominant themes emerged from the thematic content analysis: 1) Medical Applications of Opioids; 2) Problematic Opioid Use; 3) Treatment and Prevention Strategies; and 4) Recommendations. Data highlighted ‘the invisible epidemic’, with treatment strategies to be specifically tailored to this population in light of metabolic differences and drug interactions as part of aging. Seniors are part of the current epidemic, with tailored approaches needed to ensure adequate, evidence-based counteraction – including seniors’ voices alongside further research in this regard. Education and training for prescribers needs to be enhanced alongside cross-jurisdictional drug monitoring programs to avoid drug interactions and misuse/fraud.
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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".