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Record W2033698622 · doi:10.1080/10884600701334952

Gambling Addiction as a Pathology: Some Markers for Empowerment

2007· article· en· W2033698622 on OpenAlexaff
Amnon Jacob Suissa

Bibliographic record

VenueJournal of Addictions Nursing · 2007
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPhenomenonPsychosocialHarmAddictionEmpowermentDiseasePsychologySocial phenomenonMedicalizationPerspective (graphical)Power (physics)Social issuesHarm reductionPsychiatryCriminologySocial psychologySociologyMedicinePolitical sciencePublic healthSocial sciencePathology

Abstract

fetched live from OpenAlex

Defined by researchers as “a silent epidemic” the gambling phenomenon is a social problem that is having negative impact on individuals, families and communities. Among these effects are seen a dismantling of community networks, weakening of family and social ties, psychiatric co-morbidity, suicides and lately more homelessness. Youth, women, elderly, deprived citizens and native communities constitute the social groups that seem to suffer more from gambling accessibility when compared to others. Without pretending to cover all these aspects, we intend, from a social critical perspective, to highlight some of the major psychosocial stakes of the gambling phenomenon. After a brief historical overview underlining the social construction of gambling as a pathology, we will address issues such as the social and ethical contradictions of governments when managing gambling and the heated debate around the disease model of addiction versus a multifactorial approach to this phenomenon. Finally, we propose markers for empowerment while comparing the disease model and the harm reduction one. We hope that these markers can contribute to transfer some power to individuals and their social networks, activate the therapeutic processes and advance the debate on the complex issues that gambling represents in our society.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.068
GPT teacher head0.433
Teacher spread0.364 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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