Shining the Light on Mental Health in a Population at Risk: Cerebral Palsy and Other Developmental Disabilities
Bibliographic record
Abstract
Editorials3 September 2019Shining the Light on Mental Health in a Population at Risk: Cerebral Palsy and Other Developmental DisabilitiesGloria Krahn, PhD, MPH and Susan Havercamp, PhDGloria Krahn, PhD, MPHOregon State University, Corvallis, Oregon (G.K.)Search for more papers by this author and Susan Havercamp, PhDOhio State University, Columbus, Ohio (S.H.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M19-1951 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail The health, both physical and mental, of persons with developmental disabilities is recently coming out of the shadows and gaining attention in research, policy, and practice. In a carefully designed study, Whitney and colleagues (1) report that adults with cerebral palsy (CP), with or without co-occurring developmental disabilities, experience much higher rates of mental health disorders than adults without CP. These findings are important to health professionals, policymakers, and researchers to understand, address, and potentially prevent mental health disorders within the context of promoting overall health.Whitney and colleagues' findings significantly contribute to an emerging literature of population-based studies that ...References1. Whitney DG, Warschausky SA, Ng S, et al. Prevalence of mental health disorders among adults with cerebral palsy. A cross-sectional analysis. Ann Intern Med. 2019;171:328-33. doi:10.7326/M18-3420 LinkGoogle Scholar2. Craig F, Savino R, Trabacca A. A systematic review of comorbidity between cerebral palsy, autism spectrum disorders and attention deficit hyperactivity disorder. Eur J Paediatr Neurol. 2019;23:31-42. [PMID: 30446273] doi:10.1016/j.ejpn.2018.10.005 CrossrefMedlineGoogle Scholar3. Reichard A, Haile E, Morris A. Characteristics of Medicare beneficiaries with intellectual or developmental disabilities. Intellect Dev Disabil. 2019. [Forthcoming]. CrossrefMedlineGoogle Scholar4. Shooshtari S, Martens PJ, Burchill CA, et al. Prevalence of depression and dementia among adults with developmental disabilities in Manitoba, Canada. Int J Family Med. 2011;2011:319574. [PMID: 22295184] doi:10.1155/2011/319574 CrossrefMedlineGoogle Scholar5. Scheepers M, Kerr M, O'Hara D, et al. Reducing health disparity in people with intellectual disabilities: a report from Health Issues Special Interest Research Group of the International Association for the Scientific Study of Intellectual Disabilities. J Policy Pract Intellect Disabil. 2005;2:249-255. doi:10.1111/j.1741-1130.2005.00037.x CrossrefGoogle Scholar6. Wilkinson J, Dreyfus D, Cerreto M, et al. “Sometimes I feel overwhelmed”: educational needs of family physicians caring for people with intellectual disability. Intellect Dev Disabil. 2012;50:243-50. [PMID: 22731973] doi:10.1352/1934-9556-50.3.243 CrossrefMedlineGoogle Scholar7. Substance Abuse and Mental Health Services Administration. Guidance on inappropriate use of antipsychotics: older adults and people with intellectual and develomental disabilities in community settings. Accessed at https://store.samhsa.gov/system/files/pep19-inappuse-br_0.pdf on 18 June 2019. Google Scholar8. Alliance for Disability in Health Care Education. Core Competencies on Disabilty for Health Care Education. Peapack, NJ: Alliance for Disability in Health Care Education; 2018. Accessed at http://nisonger.osu.edu/wp-content/uploads/2018/09/Core-Competencies-on-Disability_8.31.18.pdf on 18 June 2019. Google Scholar9. American Medical Association. Inclusion of Developmental Disabilities Curriculum in Undergraduate, Graduate and Continuing Medical Education of Physicians. Accessed at www.ama-assn.org/sites/ama-assn.org/files/corp/media-browser/public/hod/a17-resolutions.pdf on 18 June 2019. Google Scholar10. National Council on Disability. NCD response letter to Liaison Committee on Medical Education regarding integration of disability curriculum requirement. Accessed at www.ncd.gov/publications/2019/ncd-response-letter-lcme on 18 June 2019. Google Scholar Author, Article, and Disclosure InformationAffiliations: Oregon State University, Corvallis, Oregon (G.K.)Ohio State University, Columbus, Ohio (S.H.)Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M19-1951.Corresponding Author: Gloria Krahn, PhD, MPH, Oregon State University, 2631 SW Campus Way, Corvallis, OR 97331; e-mail, Gloria.[email protected]edu.Current Author Addresses: Dr. Krahn: Oregon State University, 2631 SW Campus Way, Corvallis, OR 97331.Dr. Havercamp: Ohio State University, 1581 Dodd Drive, Columbus, OH 43210.This article was published at Annals.org on 6 August 2019. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoPrevalence of Mental Health Disorders Among Adults With Cerebral Palsy Daniel G. Whitney , Seth A. Warschausky , Sophia Ng , Edward A. Hurvitz , Neil S. Kamdar , and Mark D. Peterson Metrics 3 September 2019Volume 171, Issue 5Page: 370-371KeywordsAdultsAnxiety disordersCerebral palsyCommunication in health careDisabilitiesHealth careIntellectual disabilityMedicarePsychiatry and mental healthYoung adults ePublished: 6 August 2019 Issue Published: 3 September 2019 Copyright & PermissionsCopyright © 2019 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".