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Record W4386862033 · doi:10.32799/ijih.v18i2.39270

Conversations on Cannabis and Mental Health: Recommendations for Health and Social Care Providers from Indigenous 2SLGTBQQIA+ People in Canada

2023· article· en· W4386862033 on OpenAlexafffundvenueabout
Marisa Blake, Jessica R. Webb, Lee Allison Clark, Chaneesa Ryan, Abrar Ali, Lynne Groulx

Bibliographic record

VenueInternational Journal of Indigenous Health · 2023
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsNative Women's Association of Canada
FundersMental Health CommissionMcMaster UniversityAmerican Heart Association
KeywordsIndigenousMental healthLegalizationCannabisHealth carePublic relationsMedicineNursingPsychologyPolitical sciencePsychiatryLaw

Abstract

fetched live from OpenAlex

With the recent legalization of cannabis in Canada, there is an urgent need for information about its effects on Indigenous populations due to the impact of cannabis on the mental health of Indigenous Peoples in Canada being largely unknown. Using the guiding principles of Etuaptmumk (Two-Eyed Seeing), Sharing Circles were held to hear the needs and experiences of Indigenous People in relation to their mental health and cannabis use. From these engagements and using gender-based and distinctions-based analysis, four recommendations were developed for academic institutions, medical regulatory authorities and health and social care providers (HSCPs) to consider when caring for Indigenous People living with mental health issues. The findings point to the disconnection between recent research on medical cannabis and its availability to Indigenous People through accessible mediums, HSCPs, and the lack of cultural safety in health and social services. The four recommendations provided are helpful to both educate frontline HSCPs about the needs and experiences of Indigenous People and improve access to current information and best practices for Indigenous People who use cannabis for mental health from the regulatory and representation perspective.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.356
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes4
Has abstractyes

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