De las Redes Sociales a las Intervenciones en Salud Basadas en Redes Sociales
Bibliographic record
Abstract
Los siguientes texto corresponden a los abstract de los workshop del International Forum on Health Care and and Information Communication Technology (HICT) , celebrado en Barcelona, entre el 8 y el 10 de marzo de 2011. Organizado por el Laboratorio de Psicologia y Tecnologia (LabPsiTec), de la Universidad Jaume I de Castellon y de la Universidad de Valencia; el Grupo de Psicologia, Salud y Red (PSiNET) de la Universitat Oberta de Catalunya y el Centre for Research in Family Health, del IWK Health Centre, de Canada. Se trata del primer foro interna cional de estas caracteristicas, cuyo objetivo fundamental es crear un espacio comun para investigadores, es tudiantes y profesionales del ambito de la Salud y las Tecnologias de la Informacion y la Comunicacion. Bajo el titulo Descubriendo la Amplia Gama de Usos de las TIC en la Salud, el Foro pretendia compartir conocimientos y experiencias de investigacion para explorar como las TIC pueden ser utilizadas en el ambito de la salud para evaluar, prevenir y gestionar las cuestiones relacionadas con la salud. La Fundacion para la eSaludFeSalud, editora de la RevistaeSalud.com, quiso apoyar la celebracion de este Foro internacional, mediante la edicion en el numero 26 de la publicacion de los Astract de las conferencias, los pos ters y workshops presentados durante esta reunion cientifica, contribuyendo de esta forma a la divulgacion de actividades cientificas relacionadas con la eSalud. La RevistaeSalud.com es, por tanto, la publicion cientifica oficial del HICT 2011.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".