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Record W4392101270 · doi:10.3390/healthcare12050527

Measures for Persons with Spinal Cord Injury to Monitor Their Transitions in Care, Health, Function, and Quality of Life Experiences and Needs: A Protocol for Co-Developing a Self-Evaluation Tool

2024· article· en· W4392101270 on OpenAlexafffund
Katharina Kovacs Burns, Zahra Bhatia, Benveet Gill, Dalique van der Nest, Jason Knox, Magda Mouneimne, Taryn Buck, Rebecca Charbonneau, Kasey Aiello, Adalberto Loyola‐Sánchez, Rija Kamran, Olaleye Olayinka, Chester Ho

Bibliographic record

VenueHealthcare · 2024
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsFoothills Medical CentreGlenrose Rehabilitation HospitalSpinal Cord Injury AlbertaUniversity of AlbertaAlberta Health Services
FundersCanadian Institutes of Health Research
KeywordsProtocol (science)Delphi methodRehabilitationSpinal cord injuryDelphiReliability (semiconductor)Health careQuality (philosophy)MedicineFunction (biology)Process (computing)Nominal group techniqueProcess managementComputer scienceNursingKnowledge managementPhysical therapyEngineeringSpinal cord

Abstract

fetched live from OpenAlex

Evaluating the experiences of persons with spinal cord injury (PwSCI) regarding their transitions in care and changes in health, function, and quality of life is complex, fragmented, and involves multiple tools and measures. A staged protocol was implemented with PwSCI and relevant expert stakeholders initially exploring and selecting existing measures or tools through a modified Delphi process, followed by choosing one of two options. The options were to either support the use of the 10 selected tools from the Delphi method or to co-develop one unique condensed tool with relevant measures to evaluate all four domains. The stakeholders chose to co-develop one tool to be used by persons with SCI to monitor their transition experiences across settings and care providers. This includes any issues with care or support they needed to address at the time of discharge from acute care or rehabilitation and in the community at 3, 6, and 12 months or longer post-discharge. Once developed, the tool was made available online for the final stage of the protocol, which proposes that the tool be reliability tested prior to its launch, followed by validation testing by PwSCI.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.247
GPT teacher head0.524
Teacher spread0.277 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes2
Has abstractyes

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