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Record W2803658981 · doi:10.1093/pch/pxy054.134

CANADIAN PEDIATRICIANS’ VIEWS AND KNOWLEDGE ABOUT CANNABIS USE FOR MEDICAL PURPOSES AMONG CHILDREN AND ADOLESCENTS

2018· article· en· W2803658981 on OpenAlexaboutno aff
Richard E. Bélanger, Christina Grant, Myriam Côté, Elizabeth Donner, Vicky R. Breakey, Julie Laflamme, Anne-Marie Pinard, Michael Rieder

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisMedicineFamily medicineDescriptive statisticsMedical cannabisPublic healthPsychiatryNursing

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Cannabis use for medical purposes has gathered growing interest from the public through its purported benefits. Since 2001, Health Canada has authorized availability from health practitioners, even for paediatric patients, despite concerns regarding efficacy and adverse effects among children and adolescents. It is likely that a lack of knowledge regarding the substance and indications for use, tempered by known and unknown side effects, dictate paediatricians’ practice toward cannabis. OBJECTIVES This study examines the views and knowledge of Canadian paediatricians regarding the use of cannabis for medical purposes among children and adolescents. Differences between general paediatricians and sub-specialists were explored. DESIGN/METHODS Data was collected using a Canadian Paediatric Surveillance Program (CPSP) one-time survey performed in 2017. A total of 864 paediatric physicians (33% participation rate) were asked about medical use of cannabis. They were also asked personal and professional characteristics. Descriptive statistics regarding their views and knowledge towards cannabis use for medical purposes are reported, as well as significant differences (p<0.05) based on their belonging to either the general paediatricians or the sub-specialists subgroup. RESULTS General paediatricians represented 55.4% of the analyzed sample, with 36.7% having ≥ 20 years of practice. Half (50.4%) of all surveyed paediatricians had encountered in the past year patients who used cannabis for medical purposes (authorized or not). Half (50.6%) were also aware that Canadian physicians could authorize cannabis to children for medical purposes. More (61.3%) knew they could authorize it to adolescents (significantly more among sub-specialists, p=0.03). Nearly half (46.5%) believed that there are appropriate indications to support the authorization of cannabis for medical reasons to the paediatric population (significantly more among sub-specialists, p<0.01). The most common reasons for not prescribing were the lack of medical evidence about clinical efficacy (82.8%), dosing/toxicity (79.0%) and concerns about potential long-term impacts (78.4%). Most paediatricians reported little knowledge on why cannabis could be authorized (75.9%), what products may be authorized (89.1%), and how cannabis can be authorized (90.4%), sub-specialists reporting the highest knowledge on each topic (p<0.01). CONCLUSION Cannabis use for medical purposes is a situation frequently encountered by paediatricians. While they are generally supportive of certain medical indications for its authorization, most have minimal knowledge on its use and, report concerns about efficacy and safety. Differences between general paediatricians and sub-specialists probably emerge from conditions treated by these two groups. Our study highlights the need for timely continuing education for all paediatricians on this topic.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.314
Teacher spread0.299 · 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 designObservational
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

Citations5
Published2018
Admission routes1
Has abstractyes

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