MétaCan
Menu
← Back to cohort
Record W324429764 · doi:10.1177/070674371205701001

Treating Psychopathology in Adults with Developmental Disabilities: Glass Half Empty or Half Full?

2012· editorial· en· W324429764 on OpenAlexvenueno aff
Luc Lecavalier

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2012
Typeeditorial
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPsychopathologyPsychiatryComorbidityIntellectual disabilityAutismPervasive developmental disorderAutism spectrum disorderClinical psychology

Abstract

fetched live from OpenAlex

Abbreviations ADHD attention-deficit hyperactivity disorder ASD autism spectrum disorder DD developmental disability DR differential reinforcement ID intellectual disability It has long been known that behaviour and emotional problems occur at high rates in people with IDs and DDs.1 It is also well established that these problems can start at an early age and persist throughout the lifespan.2,3 Behaviour and emotional problems are costly to society and may ostracize people and their caregivers, clearly a countercurrent to the present Zeitgeist of inclusion. These problems can be quite stressful and dangerous for caregivers. For these reasons, psychopathology has long been, and continues to be, one of the central issues of DDs. The 2 comprehensive reviews45 provide an accurate depiction of the present state of affairs in IDs and ASD. In the first review, Dr Johnny L Matson and colleagues4 discuss discrepancies between clinical realities and best evidence practice. My own experience converges with these observations: psychiatric diagnoses are misunderstood and behavioural technologies are underused. Why is it so? An obvious explanation for the diagnostic challenges is that self-report is of limited value in many people with DDs, who, by definition, have impaired insight and communication skills. Until new technologies are developed, caregiver observations will be at the forefront of diagnostic endeavours. Numerous rating instruments have been developed but the gold standard continues to remain elusive. As a result, fundamental issues, such as phenomenology, prevalence, comorbidity, and course of psychopathology, are not well understood. This measurement problem impacts the possibility of replicating findings, which is at the heart of scientific progress. Simply put, we do not know to what extent intellectual deficits or ASD alter the typical clinical presentation of psychiatric syndromes. In many ways, diagnostic difficulties are inherent to DDs, especially in lower-functioning people. Suboptimal use of behavioural technology is a different story. Functional assessments are not used as much as they should be. Treatment decisions are often not data-driven. We can reflect on these issues in terms of efficacy and effectiveness. Efficacious treatments are those that prove beneficial for patients in well-controlled treatment studies. Effectiveness entai Is showing that efficacious treatment can be transported from the research setting to the community where there is more variation in subject selection and treatment implementation. No serious scientist would argue against the validity of operant conditioning. It is the short-term cost and practicalities that hamper optimal use of many behavioural methods. One of the biggest challenges to applied behavioural interventionists is undoubtedly the transfer of technology in a world with increased regulations and financial constraints and high staff burnout and turnover. In the second review, Dr Peter Sturmey5 provides a synthesis of the treatment literature in DDs. The review shows that there are many publications on the topic. Conversely, it indicates that the available evidence for treatments is quite limited. Of course, a distinction must be made between ineffective or harmful treatments and those that are not currently supported by enough quality research. Most (but not all) applied researchers would agree that rigorous treatment studies entail randomization, comparison to alternative treatments, blind evaluations, standardized outcome measures, standardized doses, and a large enough sample size for meaningful analyses and generalization of results.6,7 I sadly agree that there are too few methodologically robust studies of DDs. Why is it so? An obvious observation is that only a small proportion of the population has a DD, which makes study recruitment a serious obstacle. Imagine the researcher who wants to study the safety and efficacy of an alpha agonist on hyperactivity and aggression in adults with ASD. …

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.018
GPT teacher head0.284
Teacher spread0.265 · 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 designNot applicable
Domainnot available
GenreEditorial

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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Psychiatry→Same topicAutism Spectrum Disorder Research→French-language works237,207→