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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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score0.928

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.009
Science and technology studies0.0140.021
Scholarly communication0.0130.007
Open science0.0050.005
Research integrity0.0120.011
Insufficient payload (model declined to judge)0.0100.001

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.

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations19
Published2008
Admission routes2
Has abstractyes

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