Psychometric properties of the Bergen Facebook Addiction Scale for Portuguese adults
Bibliographic record
Abstract
Background: The research about online behavior addiction suggests the importance of improving assessment methods and deepening the knowledge about this phenomenon.Goals: To characterize main psychometric properties of the Bergen Facebook Addiction Scale (BFAS) for Portuguese adults.Methods: This cross-sectional study design involved Portuguese participants who were selected by snowball-convenience sampling. Data were collected through online survey of sociodemographic and BFAS, with invitations distributed by Facebook. Data analysis was performed through descriptive and reliability tests, as well factorial and invariance analysis.Results: The sample was composed of 444 Portuguese people between 18 and 73 years old. Reliability estimate for the one-factor solution was .795, suggesting good internal consistency reliability properties. Confirmatory factor analysis revealed congruence with original unifactorial model of BFAS. Multigroup analysis supported measurement invariance across sex and education level.Discussion: These results suggest reliability and validity. Invariance evidence was also confirmed for the BFAS in a Portuguese sample of Facebook users. These results contribute to the validation process of the Portuguese version of the BFAS, encouraging further studies with different clinical and non-clinical groups.
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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".