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Record W6976939569 · doi:10.6084/m9.figshare.20264071

Perspectives from the spinal cord injury community with teleSCI services during the COVID-19 pandemic: a qualitative study

2022· article· en· W6976939569 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2022
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careQualitative researchSpinal cord injuryLeverage (statistics)PerceptionHealthcare deliveryTelehealthPandemic

Abstract

fetched live from OpenAlex

To explore individuals with spinal cord injury (SCI) experiences with and perceptions towards teleSCI services during the COVID-19 global pandemic in British Columbia, Canada. Using maximum variation sampling, we invited selected individuals from a larger quantitative dataset (n = 71) to partake in an interview. In total, 12 individuals participated in the study. Interviews were recorded and transcribed verbatim. Interview transcripts were then coded and analysed by team members using qualitative descriptive analysis. Individuals with an SCI perceived teleSCI services to be convenient, accessible, affordable, and an effective way to access some healthcare services during the COVID-19 pandemic. However, in-person healthcare was still needed by many participants to effectively manage and treat their SCI-associated secondary conditions. Our findings suggest that, in a post-pandemic world, the SCI community would benefit from blended models of healthcare delivery that leverage telecommunication technologies to increase accessibility to healthcare while still providing in-person care for assessments and treatments.Implications for RehabilitationIndividuals with an SCI perceived teleSCI services to be convenient, accessible, affordable, and an effective way to access some healthcare services during the COVID-19 pandemic. However, in-person healthcare was still needed and desired by those with an SCI to effectively manage and treat their SCI-associated secondary conditions.In a post-pandemic world, individuals with an SCI would benefit from blended models of healthcare delivery that leverage telecommunication technologies to increase accessibility to healthcare, while still providing in-person care for those requiring ongoing treatment and management of secondary conditions associated with the patient’s SCI.TeleSCI services offer the potential to allow healthcare professionals and SCI specialists to collaborate (digitally) with patients at the same time. This patient-centered approach could not only help healthcare professionals strategize effective remedies to better manage secondary conditions associated with SCI but could result in overall better-quality care received by those within the SCI community. Individuals with an SCI perceived teleSCI services to be convenient, accessible, affordable, and an effective way to access some healthcare services during the COVID-19 pandemic. However, in-person healthcare was still needed and desired by those with an SCI to effectively manage and treat their SCI-associated secondary conditions. In a post-pandemic world, individuals with an SCI would benefit from blended models of healthcare delivery that leverage telecommunication technologies to increase accessibility to healthcare, while still providing in-person care for those requiring ongoing treatment and management of secondary conditions associated with the patient’s SCI. TeleSCI services offer the potential to allow healthcare professionals and SCI specialists to collaborate (digitally) with patients at the same time. This patient-centered approach could not only help healthcare professionals strategize effective remedies to better manage secondary conditions associated with SCI but could result in overall better-quality care received by those within the SCI community.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0170.010
Scholarly communication0.0050.004
Open science0.0020.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.216
GPT teacher head0.485
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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