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Record W6950530421 · doi:10.5683/sp2/hpyqrw

Manitoba Gambling and Problem Gambling 2006 [Canada]

2018· dataset· en· W6950530421 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2018
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRandom digit dialingPopulationSample (material)Public healthRecallAddictionDepression (economics)Telephone surveyTest (biology)

Abstract

fetched live from OpenAlex

The Manitoba Gambling and Problem Gambling 2006 Study was commissioned by the Addictions Foundation of Manitoba (AFM), an organization responsible for providing rehabilitation and prevention services for Manitoba citizens related to substance use and problem gambling. The goal of AFM’s research program is to better inform rehabilitation practice, public education, and health policy. The survey design for this study is based on the previous survey, Gambling involvement and problem gambling in Manitoba 2001. The survey questionnaire collected information with respect to: Gambling activity – Game types, frequency, financial commitments, reasons for gambling; Risk factors - Recall of big wins/losses, belief in gambling myths; Problem Gambling Severity Index; Alcohol use – Questions from the Alcohol Use Disorders Identification Test (AUDIT); Other substance use - Tobacco, marijuana etc.; Mental Health - Questions relating to anxiety, depression etc.; Demographic characteristics, and; Spirituality Random digit dialing was applied, allowing for the inclusion of residents with unlisted or new numbers. This technique produces a random sample that includes the highest possible percentage of eligible respondents. The data were weighted by gender, age and income in order to accurately represent the population of Manitoba. In total, 6007 adult residents of Manitoba were contacted by Prairie Research Associates (PRA) Inc., and asked to complete a telephone survey. Signed consent was not required as the participants were guaranteed complete anonymity. The survey response rate was 42.4%. The margin of error for the sample is +/- 1.29%, indicating accuracy 19 times out of 20.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.027
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.007
Science and technology studies0.0030.000
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0270.003

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 head0.035
GPT teacher head0.278
Teacher spread0.244 · 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 designObservational
Domainnot available
GenreDataset

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

Citations0
Published2018
Admission routes1
Has abstractyes

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