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Record W2014183546 · doi:10.1080/10282580802058429

Marijuana medicine and Canadian physicians: Challenges to meaningful drug policy reform

2008· article· en· W2014183546 on OpenAlexaffabout
Craig Jones, Andrew Hathaway

Bibliographic record

VenueContemporary Justice Review · 2008
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of GuelphQueen's University
Fundersnot available
KeywordsPsychologyPsychiatryMedicinePublic relationsFamily medicinePolitical science

Abstract

fetched live from OpenAlex

There are modest indications of movement in the glacial reform of Canada’s marijuana prohibition. A sign of formal progress is the legal exemption for seriously ill medical users under Health Canada’s evolving Marihuana Medical Access Regulations (MMAR). Several hundred patients have now been approved through an application process that requires support from a medical practitioner or specialist. Physicians are constrained from complying with the MMAR by the highly conservative stance of the Canadian Medical Association (CMA), while the Canadian Medical Protective Association – the body that acts as legal advocates for doctors – maintains a view toward marijuana that, applied to any other drug, would make prescribing even the most routine therapies difficult. On the other hand, it is not clear that physicians really take much interest in their patients’ marijuana use. This article examines the conflict between patients who choose to self‐medicate with marijuana and the community that governs physicians in Canada. It draws on findings from two studies that, respectively, explore doctors’ views on marijuana and the experiences of patients who self‐medicate with cannabis. The inherent conservatism of the medical community – reinforced by lack of interest in how such use might benefit some patients – militates against more learning or widespread acceptance of the use of cannabis as medicine. Nonetheless, the authors argue, doctors ought not to perpetuate the ignoble tradition of ‘Don’t ask – Don’t tell’.

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)0.001
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.321
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.096
GPT teacher head0.352
Teacher spread0.256 · 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
GenreReview

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

Citations19
Published2008
Admission routes2
Has abstractyes

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