401 - Cannabis and Older Adults
Bibliographic record
Abstract
The National Cannabis Survey results indicates that cannabis consumption among older adults has been accelerating at a much faster pace than other age groups in Canada. Internationally, an increasing number of countries and U.S. states have also legalized medical and non-medical cannabis.More than 1500 physicians, nurse practitioners, other healthcare providers, healthcare students, older adults and caregivers of older adults responded to a needs assessment survey on Cannabis and Older Adults distributed by the Canadian Coalition for Seniors’ Mental Health (CCSMH) in the fall of 2020.Responses showed that 89% of physicians and nurse practitioners and 76% of other healthcare providers are aware of older patients in their practice using cannabis. Despite this fact, only 39% of physicians and nurse practitioners and 26% of other healthcare providers feel strongly or very strongly that they have sufficient knowledge and expertise to address older patients’ and theircaregivers’ questions about cannabis.Older adults who responded to the survey indicated that their most common reasons for using cannabis were pain, sleep and anxiety. Fifty-one percent responded that they had talked to their doctor or healthcare provider about cannabis but 41% of those older adults stated that their doctor or healthcare provider were unable to answer their questions. Older adults reported they access information on cannabis from the internet (45%), physicians (40%), friends and family (34%), cannabis stores and clinics (28%), the media (24%), and other healthcare providers (16%). Fifty-four percent of older adult respondents who use cannabis do so with a prescription or medical authorization from their physician/nurse practitioner for medical/therapeutic reasons. One quarter of respondents indicated they use cannabis for non-medical reasons (for recreational use).Although there is a reported gap in knowledge regarding cannabis and older adults, physicians, nurse practitioners, other healthcare providers and healthcare students all reported they are eager to learn more about how to talk with patients, how to authorize and prescribe cannabis appropriately, how to mitigate risks and assess for cannabis use disorder in older adults. CCSMH will be launching a physician- accredited e-learning course on Cannabis and Older Adults in January 2022.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".