Conceptual framework for data harmonisation in mental health using the International Classification of Functioning, Disability and Health: an example with the R2D2-MH consortium

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Black, M. H., Buitelaar, J. K., Charman, T., Ecker, C., Gallagher, L., Hens, K., Jones, E., Murphy, D., Sadaka, Y., Schaer, M., Pourcain, B. S., Wolke, D., Bonnot-Briey, S., Bourgeron, T., & Bölte, S. (2024). Conceptual framework for data harmonisation in mental health using the International Classification of Functioning, Disability and Health: an example with the R2D2-MH consortium. BMJ Mental Health, 27(1), e301283. https://doi.org/10.1136/bmjment-2024-301283

Journal or book title
BMJ Mental Health
Publisher
BMJ
Volume
27
Issue
1
Pages
e301283
Publication date: 1 Nov 2024 Added to AutiHub: 5 Jul 2026 Type: Article Article language: English
Publisher source (DOI) Full text OpenAlex source

This publication is integrated into AutiHub through:

Authors

Publication authors
15
Publication authors identified as autistic
1 / 15 (6.7%)
  1. Melissa H. Black
  2. Jan K. Buitelaar
  3. Tony Charman
  4. Christine Ecker
  5. Louise Gallagher
  1. Kristien Hens
  2. Emily Jones
  3. Declan Murphy
  4. Yair Sadaka
  5. Marie Schaer
  1. Beaté St Pourcain
  2. Dieter Wolke
  3. Stéf Bonnot-Briey *
  4. Thomas Bourgeron
  5. Sven Bölte

Abstract

INTRODUCTION: Advancing research and support for neurologically diverse populations requires novel data harmonisation methods that are capable of aligning with contemporary approaches to understanding health and disability. OBJECTIVES: We present the International Classification of Functioning, Disability and Health (ICF) as a conceptual framework to support harmonisation of mental health data and present a proof of principle within the Risk and Resilience in Developmental Diversity and Mental Health (R2D2-MH) consortium. METHOD: 138 measures from various mental health datasets were linked to the ICF following the WHO's established linking rules. FINDINGS: Findings support the notion that the ICF can assist in the harmonisation of mental health data. The high level of shared ICF codes provides indications of where items may be readily harmonised to develop datasets that may align more readily with contemporary approaches to understanding health and disability. Although the linking process necessarily entails an element of subjectivity, the application of established rules can increase rigour and transparency of the harmonisation process. CONCLUSIONS: We present the first steps towards data harmonisation in mental health that is compatible with contemporary approaches in psychiatry, being more capable of capturing diversity and aligning with more transdiagnostic and neurodiversity-affirmative ways of understanding data. CLINICAL IMPLICATIONS: Our findings show promise, but future work is needed to address quantitative harmonisation. Similarly, issues related to the traditionally 'pathophysiological' frameworks that existing datasets are often embedded in can hinder the full potential of harmonisation based on the ICF.

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Cited bibliography overview

These indicators describe the bibliography cited by this publication. An author name is counted each time it appears in one cited reference, so the same person can be counted more than once. Names not yet linked to an author already present in AutiHub are treated as unknown, not as non-autistic. Last computed: 2 Oct 2026 01:00.

Cited references
32
With a DOI
27
Without a DOI, from raw bibliography text
5
2 / 32 (6.2%) cited references include at least one author identified as autistic.
References with data to complete
5 / 32 (15.6%)
References with detected author names
27 / 32 (84.4%)
Without detected author names
5
Without a structured title
5
Without a stable identifier
5
References with raw author names still to review
5
References with external metadata lookup issues
0
These indicators apply to cited references displayed on this page, after technical duplicates have been merged. A reference without a DOI can still support author statistics when a title and author names are available.
Author names detected in the cited bibliography
273
From DOI or external metadata
273
From validated raw bibliography text
0
Raw names already validated
0
Raw names still to review
5
54 / 273 (19.8%) names are linked to an author already present in AutiHub. 219 / 273 (80.2%) names are not yet linked.
Names linked to a person identified as autistic
2 / 273 (0.7%)
Calculated across all author names detected in the cited bibliography. Among names linked to an author already present in AutiHub: 2 / 54 (3.7%). Distinct people identified as autistic: 2 / 249 (0.8%).
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