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Record W4393217229 · doi:10.3390/healthcare12070731

Implementing Lived Experience Workshops in Regional Areas of British Columbia to Enhance Clinicians’ Confidence in Spinal Cord Injury Care: An Evaluation

2024· article· en· W4393217229 on OpenAlexaffabout
H. K. Prins, Scott Donia, Shannon Rockall, James Hektner, Spring Hawes, James Laskin, John Chernesky, Vanessa K. Noonan

Bibliographic record

VenueHealthcare · 2024
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of British ColumbiaMcMaster UniversityPraxis Spinal Cord Institute
Fundersnot available
KeywordsSpinal cord injuryMedicineSpinal cordPsychiatry

Abstract

fetched live from OpenAlex

In British Columbia (BC), there are challenges accessing specialized spinal cord injury care and resources. This paper evaluated the impact of spinal cord injury health educational workshops delivered in regional communities that were informed by persons with lived experience. A community survey was conducted with 44 persons with lived experience in a BC region to identify priority SCI health-related topics. Twenty-five topics were ranked from 1-14, with bowel and bladder management ranked 1 and 4, sexual health ranked 5, and pressure injuries ranked 7. Clinical perspectives on the priorities were collected from 102 clinicians in the BC region, who independently ranked 14 of these SCI topics and considered the former 4 topics to be lower clinical priority (ranked 11-14). These priorities informed a series of SCI clinical education workshops held at healthcare facilities in three regional cities. The goals were to improve clinicians' knowledge and confidence levels when managing spinal cord injury health and to facilitate person-centred care. Positive feedback demonstrated that educational workshops supported by lived experience perspectives effectively enhanced the clinicians' understanding of spinal cord injury and their priorities. Future plans include engaging more administrators as part of this initiative and conducting workshops in other regions of BC.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.183
GPT teacher head0.543
Teacher spread0.360 · 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 designObservational
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

Citations2
Published2024
Admission routes2
Has abstractyes

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