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Lower-risk substance use guidelines accessible by youth

2023· other· en· W6958691442 on OpenAlexaff

Bibliographic record

VenueFigshare · 2023
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicLeaf Properties and Growth Measurement
Canadian institutionsSimon Fraser UniversityUniversity of Victoria
Fundersnot available
KeywordsSubstance useGovernment (linguistics)Medical prescriptionCannabisHarm reductionHarmPrescription Drug MisuseDosingPublic health

Abstract

fetched live from OpenAlex

Abstract Background Lower-risk substance use guidelines (LRSUGs) are an evidence-based harm reduction strategy used to provide information to people who use drugs so they can reduce harms associated with substance use. Objectives This study aimed to identify LRSUGs accessible to youth and to characterize the recommendations within these guidelines. The overall goal is to identify gaps in current LRSUGs and to inform researchers and policymakers of the kinds of health information youth can access. Methods We conducted a digital assessment using the Google search engine to identify LRSUGs that could be identified by youth when searching for official sources of information related to commonly used substances, including cannabis, caffeine, alcohol, hallucinogens, prescription opioids, nicotine, and/or prescription stimulants. LRSUGs were coded and data were extracted from them to identify gaps. Results One hundred thirty LRSUGs were identified; most focused on alcohol (n = 40, 31%), cannabis (n = 30, 23%), and caffeine (n = 21, 16%). LRSUGs provided recommendations about dosing (n = 108, 83%), frequency of use (n = 72, 55%), and when to use (n = 86, 66%). Most LRSUGs were published by health (n = 51, 39%) and third-sector organizations (n = 41, 32%), followed by provincial/state (n = 18, 14%), government (n = 14, 11%), municipal (n = 4, 3%), and academic (n = 2, 2%) sources. Only 16% (n = 21) of LRSUGs were youth-specific and one-quarter (n = 32, 25%) of LRSUGs provided gender-specific recommendations. Most guidelines featured information on short (n = 76, 58%) and long-term (n = 69, 53%) negative effectives and positive effects of substances (n = 56, 43%). Less than half (n = 50, 38%) of LRSUGs cited evidence in support of the information they provided. Conclusions We identified several areas in the current LRSUGs for youth that need to be addressed. Among the gaps are a lack of LRSUGs developed specifically for youth, a lack of youth engagement in developing harm reduction strategies centered around them, and a lack of evidence-based LRSUGs. Youth-oriented, evidence-based LRSUGs are needed to better support youth who use substances and help them manage the negative effects of substance use.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.821
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.3040.005

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 head0.216
GPT teacher head0.270
Teacher spread0.054 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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 routes1
Has abstractyes

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