Measuring Female Gaming: Gamer Profile, Predictors, Prevalence, and Characteristics From Psychological and Gender Perspectives
Bibliographic record
Abstract
Research investigating female gaming is relatively scarce, and past research has demonstrated that men are more likely to be problematic gamers. Few studies have focused on female gamers in community samples, and those that have been published have mainly collected qualitative data in Europe. There is case study evidence suggesting clinicians are increasingly treating problem female gamers. The aim of this study is threefold: (i) to establish an international female gamer profile, (ii) to determine predictors associated with perceived internet gaming disorder (IGD), and (iii) to identify those who are potentially at risk of developing gaming addiction and its characteristics by applying a quantitative approach. A cross-sectional online survey was applied through international gaming forums recruiting 625 female gamers, assessing sociodemographics, gaming devices used and play genres, and a set of questionnaires on gaming [e.g., problem online gaming (e.g., the nine-item short-form scale to assess IGD: IGDS9-SF), female stereotypes (e.g., sex role stereotyping scale), and psychological symptoms (e.g., Symptom CheckList-27-plus)]. Female gamers from all continents reported the use of all videogames, especially popular online games using computers and consoles. The proportion of gamers with potential IGD was one per cent. Regression analyses identified several risk factors for increased scores on the IGDS9-SF, namely having achievement and social motivations, embodied presence and identification with the avatar, hostility and social phobia together with negative body image, playing Multiplayer Online Battle Arena games, Massively Multiplayer Online Role-Playing Games, and First-Person-Shooter games. Findings contribute to filling the gap in knowledge on female gaming, to aid in the applicability of problematic gaming measurements in female gamers, especially those who are at risk of problematic gaming. The purpose of this study is to enhance the validity of the current measures to diagnose problem gaming appropriately in both genders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".