MétaCan
Menu
Back to cohort
Record W2288359313 · doi:10.1044/leader.gs.21032016.np

Crowdsourcing Patient-Centered Care

2016· article· en· W2288359313 on OpenAlexaboutno aff
Mary M. Huston, Tanya Coyle

Bibliographic record

VenueASHA Leader · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
Fundersnot available
KeywordsCrowdsourcingSocial mediaInternet privacyPsychologyPlain languageSession (web analytics)Public relationsMedicineMedical educationComputer scienceWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

You have accessThe ASHA LeaderGet Social1 Mar 2016Crowdsourcing Patient-Centered CareHow can you maximize the benefits of crowdsourcing treatment tips or solving puzzling cases through social media—without risking patient privacy? Mary Huston, andMS, CCC-SLP Tanya CoyleMS Mary Huston Google Scholar More articles by this author , MS, CCC-SLP and Tanya Coyle Google Scholar More articles by this author , MS https://doi.org/10.1044/leader.GS.21032016.np SectionsAbout ToolsAdd to favorites ShareFacebookTwitterLinked In Audiologists and speech-language pathologists regularly create goals and carry out treatment plans for aural rehab, stroke rehab, language intervention, articulation and much more. Many of us turn to social media outlets—Twitter, Facebook, LinkedIn, Instagram, Pinterest—for ideas to help develop these goals or plans. However, this might not always serve the best interest of our patients or clients. When it comes to patient-centered care, we might get tempted by the ease of crowdsourcing resources, ideas or suggestions through social media. A well-intentioned audiologist or SLP might begin a Facebook or Twitter discussion about specific clients or difficulties attaining certain goals. It’s wonderful to exchange ideas and brainstorm with colleagues, but doing so on public forums like social media often infringes on privacy, particularly if you ask about a rare disorder, disease or circumstance in conjunction with a particular client. Imagine how you might feel if your patients or their caregivers read one of those posts? Imagine how you’d feel if your loved one’s specialist wrote a similar post? Most of us understand the inappropriateness of sharing client-specific information over public channels, but leaving out names isn’t enough. If a client, patient, student, caregiver, family member, friend—or anyone connected to the person you discuss—guesses who the post concerns, it constitutes a confidentiality breach. So, how can we use social media to aid in our patient-centered approach? Take advantage of it as a fantastic means for learning more about patient-centered care. A quick search on Facebook reveals several groups devoted to the topic as well as numerous discussion forums. Many of these groups talk about patient-centered outcomes research used to address questions and concerns of patients and their caregivers. The medical field recently began using #PatientCenteredCare, which allows you to perform a quick search yielding relevant discussions and links to articles, podcasts and other resources. Examples of tweets that have used this hashtag include links to information about applying social network science to spread empathy to caregivers, shared decision-making, asking the right questions, and this article by Kaiser Permanente, which encourages patients to take a more active role in their treatment. Use social media not as a way to talk about our patients, but as a tool to research ways to talk with our patients. A little social media research also yields client- or patient-support sources. People often want to know more about their diagnosis or treatment plan, but might not know where to start. Social media often provides easily found leads you can share, which allows patients to explore their situation from a comfortable distance before jumping into face-to-face support groups. These types of resources include following: the Stuttering Foundation on Twitter or Facebook; the Aphasia Recovery Connection; the National Aphasia Association; the Parents of Kids with Cochlear Implants support group on Facebook; the Down Syndrome Association Facebook page; and the list continues. Spending some time vetting these sites first is always a good idea. Social media continues to offer excellent tools for connecting individuals, and that hasn’t changed. What the ethics of patient-centered care dictate we do change are the questions we ask in these public forums. So, where does this leave us? It leaves us using social media not as a way to talk about our patients, but as a tool to research ways to talk with our patients. Author Notes Mary Huston, MS, CCC-SLP, is a school-based SLP in rural North Dakota. She helped form the SLP network on Twitter and collaborates internationally with colleagues via Twitter, Facebook, and her blog at www.speechadventures.com. Huston is director of app excellence at YappGuru.com. [email protected] Tanya Coyle, MS, is a school-based SLP in Ontario, Canada, who helped establish the speech-language community on Twitter in 2010. She proposed and has managed the Leader’s “Get Social” series since its August 2013 launch. [email protected] Advertising Disclaimer | Advertise With Us Advertising Disclaimer | Advertise With Us Additional Resources FiguresSourcesRelatedDetails Volume 21Issue 3March 2016 Get Permissions Add to your Mendeley library History Published in print: Mar 1, 2016 Metrics Current downloads: 554 Topicsleader_do_tagasha-article-typesleader-topicsCopyright & Permissions© 2016 American Speech-Language-Hearing AssociationLoading ...

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)0.013
metaresearch head score (Gemma)0.034
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: Commentary · Consensus signal: none
Teacher disagreement score0.080
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0070.003
Open science0.0030.016
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0800.029

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 head0.118
GPT teacher head0.383
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueASHA LeaderSame topicSocial Media in Health EducationFrench-language works237,207