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 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".