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Record W3108292176 · doi:10.1055/s-0040-1716867

The Importance of Assessing and Treating Mental Health in Transition-Aged Adolescents with Epilepsy: A 1-Year Follow-up

2020· article· en· W3108292176 on OpenAlexaff
Sarah Healy, Tadeu A. Fantaneanu, Sharon Whiting

Bibliographic record

VenueJournal of Pediatric Epilepsy · 2020
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of OttawaOttawa HospitalChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMental healthPsychiatryEpilepsyMedicineQuality of life (healthcare)Clinical psychologyPsychologyNursing

Abstract

fetched live from OpenAlex

Abstract Mental health issues become especially problematic when adolescents with epilepsy are preparing to transition from pediatric to adult care. Consistent with guidelines, a transition clinic with ongoing mental health assessment was created, providing treatment to patients scoring in the moderate severity range or higher. In order to examine the effectiveness of our epilepsy transition clinic and the impact of mental health in transition-aged adolescents, baseline and one-year follow-up data were compared in 36 participants (M = 15.82 years, 24 males). Results showed that the majority of participants had improved or comparable mental health scores at follow-up. Furthermore, participants who met threshold for mental health treatment had significantly improved mental health (t = 3.19, p = 0.015), while those who did not showed worsened mental health (t = − 2.50, p = 0.019). Looking specifically at mental health impact, those with worsened mental health showed significantly worsened quality of life (t = 3.35, p = 0.012). Furthermore, those without mental health issues showed improved transition skills (t = − 3.86, p = 0.002), while those with mental health issues did not. Results suggest that the transition clinic is effective in helping transition-aged adolescents with their mental health. Additionally, findings suggest that addressing these mental health issues are essential to ensuring successful transitions and the best outcomes in these patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.365
Teacher spread0.322 · 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 teacher head, 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

Citations2
Published2020
Admission routes1
Has abstractyes

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