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Record W4399246911 · doi:10.3389/phrs.2024.1606948

Harnessing the Benefits of Telehealth in Long COVID Service Provision

2024· article· en· W4399246911 on OpenAlexaboutno aff
Naomi C A Whyler, Liz Atkins, Prue Hogg, Julie Metcalfe, Michelle J. L. Scoullar, Emma Tippett

Bibliographic record

VenuePublic health reviews · 2024
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakBusinessSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)TelemedicineService (business)MedicineVirologyMarketingHealth careEconomic growthPathologyEconomicsOutbreak

Abstract

fetched live from OpenAlex

Re:Luo S, Zheng Z, Bird SR, Plebanski M, Figueiredo B, Jessup R, Stelmach W, Robinson JA, Xenos S, Olasoji M, Wan DWL, Sheahan J and Itsiopoulos C (2023) An Overview of Long COVID Support Services in Australia and International Clinical Guidelines, With a Proposed Care Model in a Global Context. Public Health Rev 44: 1606084.We commend Luo et al. for their in-depth analysis of the current available services in Australia to support people with Long COVID1. This devastating illness affects over 10% of those after acute COVID-19 infection and is projected to affect over 200 million people in the next decade worldwide2. Many with Long COVID are severely impacted by physical symptoms to the extent that simple activities of daily living are extremely fatiguing, and the demands of travel to a healthcare appointment can trigger episodes of severe post-exertional malaise which has been found to affect over 80% of those with Long COVID3. Provision of healthcare should be available in a format that does not worsen symptoms nor impact upon them financially.As Luo et al. describe, options for Long COVID specialist care in Australia are limited, particularly for people in rural or remote locations1. We note, however, that their summary did not review the option of telehealth as a model of care for Long COVID. Telehealth encompasses provision of medical assessment, diagnosis, treatment, and education through the use of technology, including video and telephone-based consultation4. Our Australian-based clinic, which was not included in Luo et al.’s review, uses a telehealth model of care and to date has provided care to over 500 people with Long COVID (including children) from all states and territories, including the Northern Territory, which has no other dedicated Long COVID services1. Of our cohort, 22% live outside of major metropolitan centres as measured by the Modified Monash model5 (Figure 1).Figure 1. Geographical spread of cohort by Modified Monash Model5 classification (unpublished data)(Footnote 1).This model of care provides an option for patients with physical6 or other disability and geographical limitations7 to equitably access healthcare without physical detriment or disproportionate financial penalty due to travel costs. The need, strengths, safety and limitations of telehealth services to provide rapid and accessible care has been highlighted throughout the COVID-19 pandemic. Systemic changes within the Australian health system provided funding of a wide-scale shift in the modality of care delivery4, and which have been trialled elsewhere including Canada8.The use of telehealth, where service is otherwise limited, provides a real option for many patients to receive care they would not otherwise be able to access6, and the inability to undertake a physical examination can often be mitigated through close collaboration with the person’s primary care provider. This approach has been successfully demonstrated in several settings including with rehabilitation9, an important facet of long COVID care. Furthermore, formal and informal consumer feedback from our clinic indicates that this model of care is desired by many people with Long COVID, in keeping with published literature10. Luo et al. highlight the importance of consumer engagement and empowerment, and including consumers in discussion about models of care is of paramount importance to be able to provide optimal quality care.Provision of care for Long COVID must be equitable, should not exacerbate symptoms, and should be designed with consumer needs and opinions at its heart. The benefits of telehealth are numerous for those with Long COVID and should be embedded within systemic strategies to enhance care.Yours sincerely,[Authors]

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
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 head0.155
GPT teacher head0.440
Teacher spread0.285 · 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 designOther design
Domainnot available
GenreCommentary

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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