Frecuencia del uso del internet y móvil en los adolescentes de las comunidades urbanas y rurales
Bibliographic record
Abstract
Objective: to analyze the frequency of Internet and mobile use; Associate the use, hours before using the Internet and mobile in urban and rural communities. Method: quantitative approach of Non-experimental design, descriptive cross-sectional study. The study consisted of five schools from four cantons: Portoviejo, Santa Ana, 24 de Mayo and Junín from the province of Manabí, chosen randomly, 711 adolescents composed the sample, 383 (53.9%) men and 328 (46.2%) women, aged between 11 and 16 years (M = 13.16, DT = 1,078) of the eighth, ninth and tenth grades of General Basic Education of the Schools (GBE) of the public sector of the Urban community 424 (59.69%) and Rural community 287 (40.4%). The evaluation instruments used are: Self-made sociodemographic questionnaire of 46 items; Questionnaire on the problematic use of new technologies (Labrador, Becoña and Villadangos, 2008) of 50 items. Results: there is an association between the use of mobile phones and the urban and rural community related to the hours before contact, offline time, and feeling bad when not using the mobile phone. Conclusion: the frequency and association of the use are analyzed, hours before connecting to the Internet and mobile phones of the urban and rural community; adolescents have a greater preference for the mobile in the urban community than in the rural community; in relation to the Internet, a greater connection in the urban than the rural one; There is greater use of mobile phones than the Internet in the Urban community than the Rural community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".