Game Usage in Pregnant Women at Early Gestation in Japan
Bibliographic record
Abstract
BACKGROUND: Gaming addiction is becoming a problem in young women. However, there has been no report on game usage in pregnant women. OBJECTIVE: The aim of this study was to determine the current status of computer game usage and the existence of game addiction and also to determine the associations of game usage time with lifestyle, personal relationships and thoughts about games in pregnant women at early gestation. SUBJECTS & METHODS: We recruited pregnant women who received a pregnancy checkup during the first trimester. We distributed QR codes for the online survey. We conducted a web questionnaire survey including Internet Gaming Disorder Scale (IGDS) in 178 pregnant women. RESULTS: The proportion of women with game usage was 40.4%. The mean game usage time per day was 72.9 minutes. There were no pregnant women whose IGDS score was more than 5 points. We divided 72 participants into three groups by tertile according to game usage time per day: group A (≦ 30 mins), group B (> 30 and ≦ 90 mins) and group C (> 90 mins). There were no significant differences in current smoking, alcohol drinking and daily life behavior among the three groups. There were significant differences in the proportions of women who had difficulty for establishing personal relationships by face-to-face communication and who thought that they might have a game addiction among the three groups. Pregnant women with longer game usage time had a high IGDS score. CONCLUSION: We showed for the first time the proportion of pregnant women who use games in the early period of gestation. Pregnant women with longer game usage time may require careful observation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".