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Record W2415200538

Mental illness in the real world.

2014· article· en· W2415200538 on OpenAlexaboutno aff
Russell Schachar, Abel Ickowicz

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
Fundersnot available
KeywordsStimulantComorbidityPsychiatryMedicineClinical PracticeEuropean unionSchizophrenia (object-oriented programming)Mental illnessPsychologyMental healthFamily medicine
DOInot available

Abstract

fetched live from OpenAlex

In this issue of the Journal, Ben Amor and colleagues report for the first time in Canada the prevalence of combined stimulant and non-stimulant therapy of attention deficit/hyperactivity disorder (ADHD) and of switching from one drug to another during one year of treatment. Their work contributes greatly to our understanding of how drugs are used in actual clinical practice as opposed to the world of practice guidelines. They found that almost one in five children and adolescents with ADHD in Quebec received treatment that combined a stimulant with another psychotropic medication during the one-year study period and about one in five switched stimulants or to another drug. This rate is rather similar to previous reports in European samples. In Quebec, however, about 10% of ADHD patients received a combination of drugs involving atypical antipsychotics, a rate that was higher than in the European Union. The authors demonstrate that combinations and switching are more frequent in patients with a comorbid condition (about 1/3) although they could not probe more deeply into the nature of these comorbidities suggesting that those with a comorbid condition may not receive favorable response with stimulant therapy only. About 15% of patients without a comorbid condition were given a combination treatment including a small group of about 4% of patients who received an atypical antipsychotic despite the absence of a reported comorbidity. The authors carefully articulate the limitations of this study and disclose industry funding and their industry affiliations. Despite these limitations, their work demonstrates the power of public data bases for the study of clinical practice and contributes valuable information that will no doubt impact future training and clinical practice guidelines. Also featured in this edition is a paper by Easson and colleagues examining how suicide in young persons is portrayed in Canadian newspapers. This is an extremely important topic: suicide is the leading cause of non-accidental death among youth in Canada; deaths by suicide among Canadian youth outnumber all deaths by diseases of the heart, lungs, kidney, gastrointestinal system and cancer combined. We understand that ultimately suicide is the result of personal choice; nevertheless, genetic, developmental, and environmental factors are recognized as influential risk factors. Effective suicide-prevention strategies must acknowledge that risk is multifactorial; consequently, as much as it is realistically possible, contributing conditions ought to be considered. In this regard, collective societal knowledge, attitudes, and values play a fundamental role in suicide response and prevention. Easson and colleagues highlight the impact of the press addressing suicides in children and youth and the remarkable influence of media on public awareness and reaction. As mental health practitioners and researchers we must reflect on the importance of honest partnership with responsible media to enhance useful knowledge translation in order to effectively abate youth suicide and other relevant and serious public health threats.

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)0.003
metaresearch head score (Gemma)0.016
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 score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0070.005
Open science0.0010.003
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0190.005

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 head0.037
GPT teacher head0.291
Teacher spread0.255 · 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
Published2014
Admission routes1
Has abstractyes

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