On Evaluating Gadget Usage and Family Bond Importance: A Descriptive-Correlational Study
Bibliographic record
Abstract
Problems related to gadget usage and family bonds include reduced quality time, distraction, attention deficit, eroded communication skills, impacts on child development, increased family conflicts, negative parental role modeling, a digital divide, privacy concerns, sleep disruption, and emotional disconnect. This study explored the relationship between gadget usage and family bond importance using a descriptive-correlational method with 200 parents from Libagon, Southern Leyte, Philippines, selected through purposeful sampling. Data analysis involved frequency counts, arithmetic mean, and Pearson's correlation coefficient, with a pre-survey form and semi-structured interview guide. Findings showed that most respondents were females aged 40 and above with three children, whose gadget use varied by hour. Shared family dinners, with a mean of 3.10, moderately indicated family bond importance. A slight inverse relationship was found between gadget usage and family bonds. The study concludes that gadget usage alone does not significantly affect family bond perceptions, suggesting the need for more nuanced research into interacting factors. Recommendations include promoting balanced technology use through educational programs and community initiatives, funding further research, launching public awareness campaigns, and advocating for work-life balance. Policies should focus on influential factors like family dynamics and cultural values to enable effective interventions and resource allocation.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".