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Enregistrement W2288359313 · doi:10.1044/leader.gs.21032016.np

Crowdsourcing Patient-Centered Care

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

Notice bibliographique

RevueASHA Leader · 2016
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSocial Media in Health Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCrowdsourcingSocial mediaInternet privacyPsychologyPlain languageSession (web analytics)Public relationsMedicineMedical educationComputer scienceWorld Wide WebPolitical science

Résumé

récupéré en direct d'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 ...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,013
score de la tête « metaresearch » (Gemma)0,034
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,080
Score d'incertitude au seuil0,267

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0130,034
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0050,002
Communication savante0,0070,003
Science ouverte0,0030,016
Intégrité de la recherche0,0040,003
Charge utile insuffisante (le modèle a refusé de juger)0,0800,029

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,118
Tête enseignante GPT0,383
Écart entre enseignants0,265 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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