O206 Association Of Physical Inactivity, Low Fitness And Sedentary Behaviors With Blood Pressure In 8-10 Year Old Children
Bibliographic record
Abstract
The concept of cyberaddiction is far from being unanimously accepted by scientists (Ko, Yen, Yen, Chen, & Chen, 2012; Pezoa-Jares, Espinoza-Luna & Vasquez-Medina, 2012; Nadeau & et al. 2011; Perraton, Fusaro & Bonenfant, 2011. The same is true of addiction to videogames (Hellman, Schoenmakers, Nordstrom, & Van Holst 2013); Coulombe (2010); or to Facebook (Andreassen et al. 2012; Levard & Soulas, 2010). While certain researchers wished to see this condition included in the DSM-5, others question the operational and practical basis for the diagnostic criteria (Block, 2008).Through a review of litterature and results from research findings; the aim of this article is to propose a psychosocial perspective for the cyberaddiction phenomenon. By a psychosocial perspective, we mean the inclusion of social determinants (weak social ties, social exclusion, hyper individualism, poverty, unemployment, etc) and not only the individual characteristics associated with the disease model in the addiction field. To what extent social conditions and cyberaddiction behaviors constitute a potential pathology ? Can we include a psychosocial approach to gain a more general picture of this contemporary issue? In response to these questions, a contextualization and an attempt to define cyberaddiction will be followed by an analysis of some major issues in the development of this type of addiction. As a conclusion, a demonstration of the cycle of addiction on how people develop addictions, including cyberaddictions, will be done within a psychosocial perspective in order to seize the multifactorial aspects of this addiction.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".