Quelle est l’influence du genre dans la recherche de soins chez les joueurs?
Bibliographic record
Abstract
Cette étude vise à décrire l’influence du genre sur les différentes étapes amenant un joueur ayant des problèmes de jeu à prendre la décision de rechercher de l’aide. Le modèle de recherche d’aide de Goldsmith, Jackson et Hough (1988) a été utilisé pour conceptualiser les étapes de prise de décision menant à consulter des services d’aide pour un problème de jeu de hasard et d’argent. Au total, 83 participants, dont 45 femmes et 38 hommes adultes, y ont pris part. Les résultats indiquent que, comparativement aux hommes, les femmes sont plus nombreuses à habiter en couple, ont plus souvent de faibles revenus et subviennent moins fréquemment seules à leurs besoins, rapportent des conduites de jeu plus conséquentes, souffrent davantage de troubles anxieux au cours de leur vie et, enfin, consultent surtout des services non spécialisés. Au cours des 12 derniers mois, les femmes avaient consulté plus souvent les services médicaux de première ligne et avaient eu moins fréquemment recours aux services spécialisés que les hommes.The aim of this study was to describe the influence of gender on the various stages of the decision-making process that bring problem gamblers to seek help. The authors used the help-seeking model developed by Goldsmith, Jackson and Hough (1988) to conceptualize the different stages of the process that leads to consulting support services for a gambling problem. A total of 83 participants (45 females and 38 males) took part in the study. Results show that women are more likely to have a partner and to earn a lower income; they provide for their own needs less frequently than men; report more consistent gaming behaviours; are more prone to anxiety disorder during their lifetime; and consult primarily non-specialized services. In the previous 12 months, they had accessed front-line services more often and specialized services less frequently than men.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".