Turn on the screen, turn off the loneliness – analysis of risk factors for binge-watching among Polish medical and non-medical students. A web-based cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to determine the risk factors for binge-watching (BW) among Polish medical and non-medical students. MATERIAL AND METHODS: A STROBE-compliant cross-sectional observational study, was carried out in Poland from July 2022 - March 2023. The web-based survey consisted of a personal questionnaire, a Binge-Watching Behaviour Questionnaire, a Scale of Motivation for Watching TV Series, a shortened version of the Ryff Well-Being Scale, and the De Jong Gierveld Loneliness Scale. Inclusion criteria were being a student and providing informed consent to participate. The study involved 726 respondents (70.5% female) with an average age of 22.41 (SD=3.89), including 308 (44%) medical students. RESULTS: In the group of Polish medical students, the regression model was well-fitted F(10,287)=30.189; p<0.001, R2=0.496, and the risk factors were escape motivation (β=0.416; p<0.001), psychological well-being (β=-0.165; p=0.003), emotional loneliness (β=0.152; p=0.014), and social loneliness (β=-0.118; p=0.031). Among Polish students of other majors, the regression model was well-fitted F(10,378)=46.188; p<0.001, R2=0.538, and the influence of escape motivation (β=0.456; p<0.001), entertainment motivation (β=0.258; p<0.001), the psychological well-being of students (β=-0.134; p=0.004), and emotional loneliness (β=0.111; p=0.032) was demonstrated. CONCLUSIONS: Students are in a high-risk group for behavioural addiction known as binge-watching. Emotional loneliness, the desire to escape from everyday life problems, and reduced psychological well-being intensified binge-watching in all the studied groups. Entertainment motivation and social loneliness differentiated the groups of Polish medical and non-medical students in terms of BW risk factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".