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Record W4404002880 · doi:10.18103/mra.v12i10.5973

Navigating the Pandemic: Exploring Perspectives of Individuals with Spinal Cord Injury on COVID-19 Resources

2024· article· en· W4404002880 on OpenAlexaff
Pegah Derakhshan, William C. Miller, Ethan Simpson, T. Laine Scales, Farrukh Chishtie, Christopher B. McBride, Jaimie Borisoff, Julia Schmidt, W. Ben Mortenson

Bibliographic record

VenueMedical Research Archives · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsSpinal Cord Injury BCVancouver Coastal HealthInternational Collaboration On Repair Discoveries
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakVirologySpinal cord injuryMedicineSpinal cordPathologyPsychiatryDiseaseInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex

The COVID-19 pandemic severely impacted vulnerable populations, such as individuals with spinal cord injury (SCI). Concerns within this group have escalated regarding access to essential services, including caregiver support, equipment maintenance, and medical care during the pandemic. In response, multiple COVID-19 online resources tailored for individuals with SCI were developed and provided. This study aimed to investigate the perspectives of individuals with SCI (n=12) on available COVID-19 online resources and to examine the perceived usability, clarity, and applicability of the resources. In this qualitative description study, we used an online survey and semi-structured interviews to collect data. Survey results indicated that 70% of participants found the resources useful, 65% found them easy to navigate, and 60% were likely to use the information provided, with specific feedback revealing generally positive responses for prevention infographics and text-based mental health resources, mixed feedback for mental health and physical activity videos, and varied responses for caregiver resources. Based on the data from qualitative interviews, three main themes emerged, namely “Quality of information”, “Presentation” and “Delivery of Resources”. Findings highlight the need for more specific, realistic, and actionable information tailored to the SCI community, emphasizing the importance of detailed, visually appealing, and regularly updated resources to effectively support individuals with SCI during health crises.

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)0.007
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.324
GPT teacher head0.582
Teacher spread0.258 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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