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Record W3198306866 · doi:10.1177/20552173211038030

Research interrupted: The impact of the COVID-19 pandemic on multiple sclerosis research in the field of rehabilitation and quality of life

2021· article· en· W3198306866 on OpenAlexaff
Rebecca Maguire, Sinéad M. Hynes, Barbara Seebacher, Valerie J. Block, Kathleen M. Zackowski, Johanna Jonsdottir, Marcia Finlayson, Prudence Plummer, Jennifer Freeman, Barbara S. Giesser, Gloria von Geldern, Michelle Ploughman

Bibliographic record

VenueMultiple Sclerosis Journal - Experimental Translational and Clinical · 2021
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsQueen's UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)RehabilitationWilcoxon signed-rank testQuality of life (healthcare)2019-20 coronavirus outbreakPsychologyMultiple sclerosisMedicineGerontologyNursingPhysical therapyDiseasePsychiatryPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic has likely had a negative impact on rehabilitation and quality of life (QoL) research in multiple sclerosis (MS). METHOD: We explored perceived barriers to research among 87 researchers, representing 18 countries, both prior to and since COVID-19. RESULTS: A Wilcoxon signed-rank test found that significantly more researchers reported experiencing barriers to research since the onset of the pandemic compared to pre-COVID-19 (p < .001), with 78% of respondents reporting at least some barriers since COVID-19. The most commonly-cited barriers related to participant access (n = 38) and interruptions/delays to projects (n = 19). Although no gender differences were found in the number of barriers reported, female respondents were more likely to cite time or competing demands as barriers to research. Females were also more likely to perceive being negatively impacted by the pandemic compared to other genders (p = .007). CONCLUSIONS: Implications for the future landscape of rehabilitation research in MS are discussed.

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)0.015
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.649
GPT teacher head0.578
Teacher spread0.072 · 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 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

Citations7
Published2021
Admission routes1
Has abstractyes

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