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Record W3119555962 · doi:10.15353/cjds.v9i4.671

Why do challenges still exist in primary care for patients with spinal cord injury?

2020· article· en· W3119555962 on OpenAlexafffundvenue
Colleen McMillan, James Milligan, Loretta M. Hillier, Craig Bauman, Lindsay Donaldson, Joseph K. Lee

Bibliographic record

VenueCanadian Journal of Disability Studies · 2020
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsWestern UniversityHamilton Health SciencesHealth Sciences CentreCentre for Family MedicineMcMaster UniversityUniversity of Waterloo
FundersOntario Neurotrauma Foundation
KeywordsMedicineRehabilitationNursingQualitative researchPopulationFamily medicineHealth careSpinal cord injuryPrimary carePsychologyPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Despite having high healthcare needs, individuals with spinal cord injury (SCI) receive suboptimal primary care; they are less likely than able-bodied persons to receive preventive care and more likely to have unmet health care needs. The aim of this mixed quantitative (surveys) and qualitative (interviews) study was to gather primary care health provider and rehabilitation specialists’ perspectives on why these challenges persist despite the increasing body of evidence identifying delivery service gaps. Surveys were completed by 12 family physicians who referred individuals with SCI to an interprofessional primary care mobility clinic. Interviews were completed with eight SCI rehabilitation providers. Questions in both the survey and interviews were asked related to the barriers to the provision of optimal care for SCI, potential solutions, and preferred methods for knowledge dissemination. Skill and attitudinal reasons were offered for the lack of evidence to practice transfer including: the absence of patient self-management, poor access to specialists, lack of education for family practice physicians, fragmentation of community resources and co-ordination upon hospital discharge. Solutions offered included greater patient self-management, better access to specialists, specialized primary care services and provision of SCI guidelines and protocols. Participant explanations and solutions were then analyzed through a social disability lens to see if new understandings could be identified to explain the lack of uptake from research findings to clinical practice for this underserviced vulnerable population.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.107
GPT teacher head0.379
Teacher spread0.272 · 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

Citations8
Published2020
Admission routes3
Has abstractyes

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