Cannabis use in a Canadian long-term care facility: a case study
Bibliographic record
Abstract
BACKGROUND: Following the legalization of cannabis in Canada in 2018, people aged 65 + years reported a significant increase in cannabis consumption. Despite limited research with older adults regarding the therapeutic benefits of cannabis, there is increasing interest and use among this population, particularly for those who have chronic illnesses or are at end of life. Long-term Care (LTC) facilities are required to reflect on their care and policies related to the use of cannabis, and how to address residents' cannabis use within what they consider to be their home. METHODS: Using an exploratory case study design, this study aimed to understand how one LTC facility in western Canada addressed the major policy shift related to medical and non-medical cannabis. The case study, conducted November 2021 to August 2022, included an environmental scan of existing policies and procedures related to cannabis use at the LTC facility, a quantitative survey of Healthcare Providers' (HCP) knowledge, attitudes, and practices related to cannabis, and qualitative interviews with HCPs and administrators. Quantitative survey data were analyzed using descriptive statistics and content analysis was used to analyze the qualitative data. RESULTS: A total of 71 HCPs completed the survey and 12 HCPs, including those who functioned as administrators, participated in the interview. The largest knowledge gaps were related to dosing and creating effective treatment plans for residents using cannabis. About half of HCPs reported providing care in the past month to a resident who was taking medical cannabis (54.9%) and a quarter (25.4%) to a resident that was taking non-medical cannabis. The majority of respondents (81.7%) reported that lack of knowledge, education or information about medical cannabis were barriers to medical cannabis use in LTC. From the qualitative data, we identified four key findings regarding HCPs' attitudes, cannabis access and use, barriers to cannabis use, and non-medical cannabis use. CONCLUSIONS: With the legalization of medical and non-medical cannabis in jurisdictions around the world, LTC facilities will be obligated to develop policies, procedures and healthcare services that are able to accommodate residents' use of cannabis in a respectful and evidence-informed manner.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".