Uso de HEADSS como herramienta de tamizaje psicosocial en pacientes adolescentes
Bibliographic record
Abstract
Adolescents have diverse and complex healthcare needs, including physical, cognitive, and psychosocial growth and development. In Peru, according to current statistics from the National Survey on Social Relations (ENARES), it is evident that 7 out of 10 adolescents are still victims of physical violence. Psychosocial screening is intended to be an instrument to discriminate risk in adolescents. It evaluates various problems such as depression, suicide, psychosis, anxiety, gangs, as well as alcohol, tobacco, and drug use. Despite its importance, it is often not adequately addressed. In a retrospective study in the United States with hospitalized pediatric patients, psychosocial screening is adequately completed in only 5.3%, showing the little importance given to preventive medicine. Because of this, the application of psychosocial screening in other areas has been sought. In Canada, a prospective study showed the potential of using screening in the emergency department, improving the uptake of at-risk patients.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".