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Record W7110532487

Manitoba Academic Rehabilitation Sciences COVID Interest Group (MARSCI)

2022· report· en· W7110532487 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2022
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Coronavirus disease 2019 (COVID-19)Multidisciplinary approachRehabilitationPublic policyPublic healthService (business)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreak
DOInot available

Abstract

fetched live from OpenAlex

Many individuals (10-30%) experience persistent and/or new symptoms beyond the acute COVID-19 infection, which can present regardless of initial infection severity. Commonly referred to as “Long COVID” among public advocacy groups, this post-COVID condition affects multiple body systems and is thought to reflect persistent inflammation, thrombosis, and an autoimmune reaction. The most consistent complaints of Long COVID are fatigue, shortness of breath, muscle pain and difficulty concentrating. Many with Long COVID experience loss of income, or struggle to fulfill family duties. Given that there have been over 117,000 PCR-test confirmed COVID-19 cases in Manitoba, it is likely that thousands of Manitobans are affected by Long COVID. Emerging international guidance recommends that policy makers address Long COVID through a multidisciplinary approach, including interprofessional rehabilitation services. With this in mind, we conducted an environmental scan to support and make recommendations for Long COVID management in Manitoba. Our objectives were to 1) identify policy for management of Long COVID, 2) learn about the lived experiences and advocacy priorities of people with lived experiences of Long COVID, and 3) gather information on current Long COVID services in Manitoba. We conducted web searches in July-September 2021 for a) provincial/territorial government policies related to Long COVID, b) peer-reviewed evidence syntheses and original studies about Long COVID, and c) Long COVID public advocacy groups. We collected information on current, publicly-funded Long COVID rehabilitation services in Manitoba, by consulting with service providers, managers and researchers with knowledge of the Manitoba health system. Our policy search identified frameworks for managing Long COVID in just two provinces (Alberta and Saskatchewan); both frameworks incorporate integrated, interprofessional care. We were unable to identify Long COVID policy in any other jurisdiction, and four jurisdictions indicated that Long COVID will be managed using existing programs or global budgets. Public advocacy groups consistently raised the lack of recognition, let alone care, for Long COVID. Concerns about accessibility to appropriate health services were consistently expressed by advocacy groups because established services may not be equipped to address the needs of people with Long COVID. Advocacy groups argue for specialized team-based clinics, with rehabilitation as one of the main components of Long COVID management. Our scan of existing Manitoba services indicated that current rehabilitation services are not designed for the needs of people with Long COVID. Major gaps include Long COVID rehabilitation services for children and youth, and accessible community-based interprofessional care for young and middle-aged adults. Long COVID rehabilitation programs are being developed, but are not yet funded.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.621
Threshold uncertainty score0.754

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1480.020

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 head0.080
GPT teacher head0.287
Teacher spread0.207 · 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 designNot applicable
Domainnot available
GenreOther

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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