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Record W2770807526 · doi:10.1080/09638288.2017.1400593

Using the International Classification of Functioning, Disability, and Health to identify outcome domains for a core outcome set for aphasia: a comparison of stakeholder perspectives

2017· article· en· W2770807526 on OpenAlexaff
Sarah J. Wallace, Linda Worrall, Tanya Rose, Guylaine Le Dorze

Bibliographic record

VenueDisability and Rehabilitation · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsInternational Classification of Functioning, Disability and HealthAphasiaOutcome (game theory)Core (optical fiber)Set (abstract data type)PsychologyStakeholderRehabilitationCognitive psychologyPhysical medicine and rehabilitationMedicineComputer sciencePolitical sciencePublic relations

Abstract

fetched live from OpenAlex

PURPOSE: This study synthesised the findings of three separate consensus processes exploring the perspectives of key stakeholder groups about important aphasia treatment outcomes. This process was conducted to generate recommendations for outcome domains to be included in a core outcome set for aphasia treatment trials. MATERIALS AND METHODS: International Classification of Functioning, Disability, and Health codes were examined to identify where the groups of: (1) people with aphasia, (2) family members, (3) aphasia researchers, and (4) aphasia clinicians/managers, demonstrated congruence in their perspectives regarding important treatment outcomes. Codes were contextualized using qualitative data. RESULTS: Congruence across three or more stakeholder groups was evident for ICF chapters: Mental functions; Communication; and Services, systems, and policies. Quality of life was explicitly identified by clinicians/managers and researchers, while people with aphasia and their families identified outcomes known to be determinants of quality of life. CONCLUSIONS: Core aphasia outcomes include: language, emotional wellbeing, communication, patient-reported satisfaction with treatment and impact of treatment, and quality of life. International Classification of Functioning, Disability, and Health coding can be used to compare stakeholder perspectives and identify domains for core outcome sets. Pairing coding with qualitative data may ensure important nuances of meaning are retained. Implications for rehabilitation The outcomes measured in treatment research should be relevant to stakeholders and support health care decision making. Core outcome sets (agreed, minimum set of outcomes, and outcome measures) are increasingly being used to ensure the relevancy and consistency of the outcomes measured in treatment studies. Important aphasia treatment outcomes span all components of the International Classification of Functioning, Disability, and Health. Stakeholders demonstrated congruence in the identification of important outcomes which related Mental functions; Communication; Services, systems, and policies; and Quality of life. A core outcome set for aphasia treatment research should include measures relating to: language, emotional wellbeing, communication, patient-reported satisfaction with treatment and impact of treatment, and quality of life. Coding using the International Classification of Functioning, Disability, and Health, presents a novel methodology for the comparison of stakeholder perspectives to inform recommendations for outcome constructs to be included in a core outcome set. Coding can be paired with qualitative data to ensure nuances of meaning are retained.

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)0.264
metaresearch head score (Gemma)0.323
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.908

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2640.323
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0140.009
Science and technology studies0.0040.006
Scholarly communication0.0070.007
Open science0.0020.010
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.484
GPT teacher head0.531
Teacher spread0.048 · 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.

Study designQualitative
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

Citations32
Published2017
Admission routes1
Has abstractyes

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