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Record W7005742355

Santé des jeunes et interventions socioéducatives Youth health and socio-educative interventions

2020· other· fr· W7005742355 on OpenAlexaboutno aff

Bibliographic record

VenueIndustrias Culturais (Universidade de Coimbra) · 2020
Typeother
Languagefr
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)Qualitative researchPublic healthMEDLINE
DOInot available

Abstract

fetched live from OpenAlex

La santé des jeunes fait l’objet de préoccupations importantes dans le cadre des politiques de santé publique depuis ces vingt dernières années. Les campagnes de prévention ont connu un essor particulier, ciblant principalement les conduites juvéniles considérées comme plus exposées au risque en comparaison d’autres âges de la vie. En effet, dans les représentations sociales, le temps de la jeunesse est bien souvent pensé comme une période de vulnérabilité, le plaçant ainsi au centre d’attentions multiples. Les jeunes concernés par des interventions socioéducatives font l’objet de préoccupations particulières notamment de la part des politiques publiques. La stratégie nationale de prévention et de protection de l’enfance 2020-2022 montre, à titre d’exemple, comment la santé a pris une importance croissante dans ce champ. Ce colloque a pour objectif de croiser les savoirs des champs académiques et professionnels pour penser les pratiques quotidiennes des jeunes et leurs représentations en matière de santé et de soin. Il s’agit aussi de les envisager dans un contexte d’intervention socioéducative à partir de la notion de transition, inhérente à cet âge de la vie, d’autonomie, avec les acteurs qui l’accompagnent, et de participation pour interroger les pratiques d’intervention en promotion de la santé.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.051
GPT teacher head0.336
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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