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Record W4411985551 · doi:10.15353/cjds.v12i3.1035

A rapid review informing an assessment tool to support the inclusion of lived experience researchers in disability research

2023· review· en· W4411985551 on OpenAlexvenueno aff
Damian Mellifont

Bibliographic record

VenueCanadian Journal of Disability Studies · 2023
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)Lived experiencePsychologySociologyPsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

Appreciating that progress is being made in terms of valuing researchers with lived experience in research about disability, far more efforts are nonetheless needed to redress ableism and to further advance inclusion. Addressing this policy issue, this rapid review aims to inform researchers with disability and their genuine allies about: a) scholarly discussions concerning the inclusion of researchers with lived experience of disability in lived experience led or co-produced disability research; and b) a practical disability research assessment tool to support this greater inclusion. The review was informed by thematic analysis as applied to 13 publications retrieved from a rapid review of ProQuest Central, Scopus, Education Source, Google Scholar and Google Chrome databases. An additional six publications were identified from peer suggestions and hand searches of citations. The three themes identified each inform about ways of including people with lived experience of disability in research about disability across respective areas of designing disability research, conducting disability research, and disseminating and evaluating disability research findings. This exploratory paper offers a preliminary, evidence-based assessment tool to help to include more researchers with lived experience of disability as leaders and co-producers of disability research.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.042
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.720
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0420.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.002
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.873
GPT teacher head0.685
Teacher spread0.187 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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