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Record W4309323596 · doi:10.1177/00220426221139420

Qualitative Research on Cannabis Use Among Youth: A Methodological Review

2022· review· en· W4309323596 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Drug Issues · 2022
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsWestern University
Fundersnot available
KeywordsQualitative researchCannabisData collectionQualitative propertyRecreationLegalizationPsychologyApplied psychologySociologyComputer scienceSocial sciencePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Recreational cannabis legalization has encouraged research regarding cannabis use among youth, especially the use of qualitative approaches. In fact, alcohol and drug use journals have recently encouraged qualitative submissions and provided criteria to ensure “high-quality” research. This study provides an objective account of the qualitative approaches used by researchers in this field and discusses implications for future research. A methodological review was conducted for studies published between January 2010 and November 2019. Targeted keyword searches in four research databases returned 1956 unique records. Pairs of reviewers independently screened records against eligibility criteria and charted data for study philosophical positioning, methodology, study aims, sampling, sample, data collection, and data analysis. 23 studies met the inclusion criteria. Several gaps in study quality criteria are observed: less than half of the studies specified the overarching methodology and just two stated philosophical positioning, with some methods unjustified. Implications for future research are discussed.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.034
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0340.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.698
GPT teacher head0.643
Teacher spread0.055 · 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