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Record W2958254727 · doi:10.1080/14459795.2019.1633381

Exploring psychological need satisfaction from gambling participation and the moderating influence of game preferences

2019· article· en· W2958254727 on OpenAlexaff
Jonathan Parke, Robert J. Williams, Peter Schofield

Bibliographic record

VenueInternational Gambling Studies · 2019
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsPsychologyHappinessSocial psychologySample (material)Applied psychology

Abstract

fetched live from OpenAlex

Psychological needs are satisfied through leisure participation, which in turn influences subjective well-being. The present study explored the psychological needs reported to be satisfied through gambling participation and examined associations between need satisfaction, game preferences and subjective well-being. A heterogeneous, self-selected sample of 1446 participants was recruited, through the Internet gambling provider Kindred Group Plc, for an online questionnaire survey. Five psychological need dimensions of gambling were identified, using exploratory and confirmatory factor analyses on calibration and validation samples, respectively: mastery, detachment, self-affirmation, risk and excitement, and affiliation. Challenge and mastery need satisfaction was higher for poker than for sports betting, horse racing, slots or casino table games; both self-affirmation and affiliation were also higher for poker than for sports betting and slots. By comparison, detachment was higher for slots than for sports gambling. While there were no significant variations in stress levels between the different forms of gambling, happiness ratings were lower for slots compared with sports betting and poker. This study provides insight into how distinctive patterns of play may satisfy different psychological needs and provides preliminary insights into how gambling patterns may prove adaptive or maladaptive as leisure choices.

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 score0.032
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

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

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 head0.405
GPT teacher head0.476
Teacher spread0.071 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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