MétaCan
Menu
Back to cohort
Record W3037016350 · doi:10.3389/fpsyt.2020.00578

Hypnotised by Your Phone? Smartphone Addiction Correlates With Hypnotisability

2020· article· en· W3037016350 on OpenAlexafffundabout
Jay A. Olson, Moriah Stendel, Samuel P. L. Veissière

Bibliographic record

VenueFrontiers in Psychiatry · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMcGill University
FundersCanada First Research Excellence FundMcGill University
KeywordsSmartphone addictionAddictionSession (web analytics)PhoneHypnosisPsychologySmartphone appBehavioral addictionClinical psychologyPositive correlationCorrelationSample (material)MedicinePsychiatryInternet privacyComputer scienceWorld Wide WebAlternative medicine

Abstract

fetched live from OpenAlex

Hypnosis and heavy smartphone use are both characterised by absorbed states in which one loses track of time and responds automatically to stimuli. In this pre-registered study, we tested whether there was a relationship between smartphone addiction and hypnotisability: one's tendency to follow suggestions under hypnosis. Over 11 public lectures, we hypnotised 641 student-aged participants; after the hypnosis session, participants completed the Smartphone Addiction Scale (Short Version). There was a positive correlation between hypnotisability and smartphone addiction (r = .17, 95% CI [.09, .24], p < .001) with a magnitude similar to standard predictors of hypnotisability. This correlation was small but unlikely spurious: it was positive in 10 of the 11 samples (including 2 from psychology courses) and persisted in a follow-up several months later. The addiction scores in this Canadian sample were unexpectedly high (M = 31.41) compared to other countries. We hypothesise that targeting the absorbed, time-distorted, and automatic use of smartphones may promote healthier phone habits.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.316
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.248
Teacher spread0.240 · 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 teacher head, 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

Citations25
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueFrontiers in PsychiatrySame topicImpact of Technology on AdolescentsFrench-language works237,207