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Record W2024579151 · doi:10.1310/sci2101-49

A Narrative Literature Review to Direct Spinal Cord Injury Patient Education Programming

2015· review· en· W2024579151 on OpenAlexaff
Kim van Wyk, Amber Backwell, Andrea Townson

Bibliographic record

VenueTopics in Spinal Cord Injury Rehabilitation · 2015
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsInternational Collaboration On Repair DiscoveriesGF Strong Rehabilitation CentreVancouver Coastal Health Research InstituteUniversity of British Columbia
FundersStrong
KeywordsMedicineRehabilitationInclusion (mineral)Qualitative researchMedical educationCritical appraisalSpinal cord injuryNarrativePatient educationNursingPhysical therapyAlternative medicinePsychology

Abstract

fetched live from OpenAlex

PURPOSE: To summarize the evidence on SCI-related education literature, while looking at potential barriers, solutions, benefits, and patient preferences regarding SCI patient education. METHOD: A literature review was conducted using 5 electronic databases. Quality appraisal instruments were designed to determine the methodological rigor of the quantitative and qualitative studies found. Selected articles were read in their entirety and themes were abstracted. RESULTS: Fourteen articles met the inclusion criteria for this narrative literature review, all of which were based on research studies. Seven of these 14 were quantitative studies, 3 were qualitative studies, and 4 were mixed-methods studies. CONCLUSION: To improve SCI education during rehabilitation, programs should maximize the receptiveness of newly injured patients to SCI-related information, optimize the delivery of SCI education, increase the number of opportunities for learning, promote and support lifelong learning, and include patient and program evaluation. How these strategies are specifically implemented needs to be determined by program management in consultation with various stakeholders, whilst considering the unique characteristics of the rehabilitation facility.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.073
GPT teacher head0.485
Teacher spread0.412 · 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 designOther design
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

Citations39
Published2015
Admission routes1
Has abstractyes

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