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Record W6957935272 · doi:10.6084/m9.figshare.16830598

Principles to guide spinal cord injury research partnerships: a Delphi consensus study

2021· article· en· W6957935272 on OpenAlexaff

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of ManitobaGeorge & Fay Yee Centre for Healthcare InnovationUniversity of British Columbia
Fundersnot available
KeywordsDelphi methodGeneral partnershipSpinal cord injuryDelphiLikert scaleMEDLINEResearch designEvidence-based medicine

Abstract

fetched live from OpenAlex

To establish consensus regarding principles that should be used to guide spinal cord injury (SCI) research partnerships between researchers and research users. A three-round Delphi consensus exercise was carried out with researchers and/or research users involved in one or more SCI research partnerships. Participants considered a list of 125 partnership principles. In rounds 1 and 2, participants rated their agreement that a principle should guide SCI research partnerships on an 11-point Likert scale. After each round, principles that received a mean score of ≥8.0 or 70% of participants rated the principle ≥8.0 were retained. In round 3, participants categorized principles as essential, desirable, irrelevant, or unsure. At least 20 individuals participated in each round. In round 1, 103 principles met consensus criteria and eight principles were added. In round 2, 93 principles met the criteria. In round 3, 29 principles were categorized as essential and eight as desirable. Recommended principles focused on the interpersonal, relational, and logistical aspects of partnerships. Principles that did not reach consensus related to social justice and actionable impact. Findings provide insight into 37 principles that could be used to combat tokenism and inform future guidance to meaningfully engage partners in SCI research.Implications for RehabilitationConsensus-based research partnership principles (i.e., norms or beliefs) were identified and could be prioritized to help support spinal cord injury (SCI) researchers and research users combat tokenism and meaningfully engage research users as partners in the co-creation of knowledge.The resulting list of recommended research partnership principles was used to inform the development of guidance to support quality partnerships between SCI researchers and research users within and outside the rehabilitation context (www.IKTprinciples.com).Guidance supporting meaningful research partnerships may accelerate the time between discovery and use of research in practice. Consensus-based research partnership principles (i.e., norms or beliefs) were identified and could be prioritized to help support spinal cord injury (SCI) researchers and research users combat tokenism and meaningfully engage research users as partners in the co-creation of knowledge. The resulting list of recommended research partnership principles was used to inform the development of guidance to support quality partnerships between SCI researchers and research users within and outside the rehabilitation context (www.IKTprinciples.com). Guidance supporting meaningful research partnerships may accelerate the time between discovery and use of research in practice.

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)0.262
metaresearch head score (Gemma)0.162
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: Qualitative
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.262
Threshold uncertainty score0.910

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2620.162
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.004
Science and technology studies0.0090.007
Scholarly communication0.0060.007
Open science0.0040.019
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.853
GPT teacher head0.624
Teacher spread0.230 · 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
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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