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P207 NRAS Virtual Groups

2022· article· en· W4224311698 on OpenAlexaff
Naomie Bwalya, Janet Brewer

Bibliographic record

VenueLara D. Veeken · 2022
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsArthritis Society
Fundersnot available
KeywordsNeuroblastoma RAS viral oncogene homologMedicineFeelingPromotion (chess)Medical educationPsychologySocial psychologyCancer

Abstract

fetched live from OpenAlex

Abstract Background/Aims NRAS Groups have long provided an opportunity for those with RA to meet others in a similar situation for mutual benefit. Attendees have told us how they enjoy meeting in a non-clinical environment to learn more about the condition and receive encouragement from others. When COVID-19 hit and NRAS groups were unable to meet in person and most people living with RA had to shield this intensified the feelings of isolation. NRAS responded to this need by establishing online regional groups and JoinTogether virtual groups. Methods 1. NRAS offered training and support to facilitate online group meetings to its regional groups’ leaders. Volunteers were provided with a dedicated NRAS email address, access to an Office 365 portal and Zoom account and GDPR training. NRAS colleagues attended introductory meetings to support the Group Leaders and continue to provide technical support, promotion via website and social media as well as general advice. 2. Recognising a need to reach a wider audience who were not accessing the regional groups - i.e. younger and perhaps “time poor” due to working etc. - NRAS took advantage of the move to online engagement and also initiated the exclusively online JoinTogether topic-based groups, using a Volunteer Lead model. Results Regional Groups: Almost half of the regional groups signed up to the online training. Many found that the online meetings brought very positive benefits e.g. they were able to reach a wider audience as attendees were not put off by having to travel and could still attend if they were feeling fatigued. Many reported it was easier to attract NHS rheumatology health professionals to give talks as they did not have to travel and could even join meetings from home in the evenings. Some groups in adjoining areas joined forces so they could expand their offerings. JoinTogether Groups: Volunteer Lead, with NRAS support, has now set up 5 topic-based groups, each led by two co-ordinators. Topics are: Exercise & Back to Sport; Parenting With Inflammatory Arthritis; 18-35 year olds with RA or JIA: Working with Inflammatory Arthritis and Parents with children with JIA. These groups are thriving and attracting new audiences. They are very much volunteer led and attendees play a key role in directing the development of the JoinTogether groups to suit their needs. Conclusion NRAS virtual groups have allowed those living with RA or JIA to maintain contact with a community, with shared experiences, throughout the pandemic. They have also been instrumental in attracting attendees from audiences NRAS had traditionally found harder to access. The Volunteer Lead model that has been successfully implemented for the JoinTogether groups can now be expanded to other areas, enabling NRAS to increase capacity for delivering vital services. Disclosure N. Bwalya: None. J. Brewer: None.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.543
Threshold uncertainty score0.652

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0020.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.5430.229

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 head0.020
GPT teacher head0.285
Teacher spread0.265 · 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.

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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