Digital Life Balance and Need for Online Social Feedback: Cross–Cultural Psychometric Analysis in Brazil
Bibliographic record
Abstract
In the contemporary digital era, the extensive integration of information and communication technologies (ICT) has significantly changed offline activities, including communication, shopping, and media consumption. This integration has been accelerated by the COVID‐19 pandemic, leading to increased reliance on ICT for work, education, socializing, and accessing essential services. Consequently, there is growing concern about the impact of ICT on well‐being, particularly regarding psychological and financial health, as well as the association with psychiatric disorders. This study is aimed at exploring the psychometric properties of two scales adapted for the Brazilian context: the Digital Life Balance (DLB) scale and the Need for Online Social Feedback (NfOSF) scale. These scales measure individuals’ perceived balance between online and offline activities and their need for social validation online, respectively. Using a sample of 220 Brazilian individuals (50.9% female, 43.6% male, mean age = 34.96 years, SD = 11.32), we conducted confirmatory factor analysis (CFA) to assess the scales’ factor structures and test the reliability and validity of the two measures. The results demonstrated good fit indices and reliable internal consistency for both scales. Additionally, metric invariance between Brazilian and Italian samples was established, supporting cross‐cultural applicability. External validity was examined through correlations with time spent on social media and the perceived importance of followers. Findings indicate that higher DLB is associated with less time spent online, while greater NfOSF correlates with higher importance placed on social media followers. These insights highlight the importance of understanding digital balance and the role of social feedback in ICT use, contributing to the effective screening of potential dysfunctional ICT use in Brazil. As a result of this study, validated Brazilian versions of the NfOSF and DLB scales were successfully obtained, offering valuable tools for assessing DLB and the NfOSF in the Brazilian context.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".