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Record W4412212017

Stress og psykisk lidelse blandt forældre til autistiske børn

2024· article· da· W4412212017 on OpenAlexaff
Ole Skov

Bibliographic record

VenueUniversity of Southern Denmark Research Portal (University of Southern Denmark) · 2024
Typearticle
Languageda
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsGeology
DOInot available

Abstract

fetched live from OpenAlex

Mette Elmose Andersen og Ole Skov, Institut for Psykologi Syddansk Universitet Forskning har vist en højere grad af stress og flere tegn på psykisk lidelse blandt forældre til autistiske børn sammenlignet med andre grupper. Eksisterende forskning om forældre stress og psykisk lidelse er primært fra USA og Australien og inkluderer ofte kun (eller primært) mødre. Nogle studier har undersøgt hvordan grad af stress og psykisk lidelse hænger sammen med andre forældre- eller børnekarakteristika, og enkelte studier har også inkluderet information om kontekst, f.eks. oplevelsen af social støtte. Det er dog de færreste studier, der har undersøgt, hvordan de forskellige karakteristika påvirker hinanden over tid. I workshoppen præsenteres dele af resultaterne fra et dansk studie, hvor 885 forældre til mindst et autistisk barn har deltaget gennem 3 dataindsamlinger over 2 år. Mere viden kan hjælpe til at tydeliggøre: * Behovet for og vigtigheden af relevant støtte til familier med autistiske børn * Hvor og hvordan der kan forebygges eller interveneres En indsats for at mindske stress og psykisk lidelse blandt forældre har potentialet til også at skabe positiv forandring for familien og det autistiske barn.

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)0.011
metaresearch head score (Gemma)0.029
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 score0.122
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0090.004
Open science0.0010.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.1220.037

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 head0.031
GPT teacher head0.288
Teacher spread0.257 · 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
Published2024
Admission routes1
Has abstractyes

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