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Record W4212972489 · doi:10.12927/hcq.2022.26713

Development of Pediatric Hospital Position Statements on Medical and Non-Prescribed Cannabis

2022· article· en· W4212972489 on OpenAlexaffvenueabout
Jonathan Whelan, Luke Edgar, Michelle Ward, Kimmo Murto, Régis Vaillancourt

Bibliographic record

VenueHealthcare Quarterly · 2022
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsOntario Centre of Excellence for Child and Youth Mental HealthSaint John Regional HospitalUniversity of Ottawa
Fundersnot available
KeywordsCannabisMedical cannabisPosition (finance)MedicineWork (physics)Best practicePediatric hospitalAction (physics)Position paperNursingMedical emergencyFamily medicineBusinessPsychiatryPolitical sciencePediatrics

Abstract

fetched live from OpenAlex

The pediatric demand for medical cannabis has been increasing. This has necessitated the need to develop hospital statements and policies at the Children's Hospital of Eastern Ontario (CHEO) to provide clinicians and administrators with recommendations for working with patients and caregivers seeking the use of prescribed or non-prescribed cannabis. Through a structured working group, two hospital position statements and policies on the pediatric use of medical and non-prescribed cannabis were developed for patients served at CHEO. In highlighting the procedural framework and position statements, these policies provide valuable recommendations and resources to other hospitals seeking to develop similar administrative action. In a changing medical landscape, best practices and policies are needed for hospital administrators on the patient use of medical and non-prescribed cannabis. The authors highlight recent policy work and position statements from CHEO, providing a valuable reference to all pediatric and adult hospitals.

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)0.079
metaresearch head score (Gemma)0.138
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 score0.079
Threshold uncertainty score0.419

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.138
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0080.004
Scholarly communication0.0070.005
Open science0.0030.006
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0080.003

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 head0.017
GPT teacher head0.340
Teacher spread0.324 · 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

Citations1
Published2022
Admission routes3
Has abstractyes

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