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Record W3205298504 · doi:10.1037/pha0000528

A novel remote TSST procedure reliably increases stress reactivity in cannabis users: A pilot study.

2021· article· en· W3205298504 on OpenAlexaff
Stephanie Collins Reed, Alyssa B. Oliva, Samantha G. Gomez, Suzette M. Evans

Bibliographic record

VenueExperimental and Clinical Psychopharmacology · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsBritish Columbia Centre on Substance Use
Fundersnot available
KeywordsTrier social stress testCravingStressorCannabisAnxietyPsychologyStress measuresMedicineStress (linguistics)Clinical psychologyPsychiatryAddictionFight-or-flight response

Abstract

fetched live from OpenAlex

= 15). The use of a remote platform such as Zoom allowed the participant and the committee to interact in real time while limiting in-person contact. The primary aim of this study was to test the feasibility of a remote version of the TSST in producing an increase in subjective stress response, cannabis craving, and cardiovascular stress in individuals who use cannabis. Participants completed subjective effects questionnaires and had blood pressure (BP) assessed before (baseline) and at various time points after the TSST. Heart rate (HR) was continuously measured throughout the session. This remote version of the TSST significantly and robustly increased State Anxiety and Perceived Stress scores, BP, and HR compared to baseline. There was no effect of the remote TSST on cannabis craving. Overall, the remote version of the TSST appears to be an effective laboratory stressor for future stress reactivity studies. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.101
GPT teacher head0.502
Teacher spread0.400 · 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 designNon-randomized trial
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

Citations4
Published2021
Admission routes1
Has abstractyes

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