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Enregistrement W1565675407 · doi:10.1044/leader.gs.20032015.np

Using Social Media for Professional Advancement: Lessons Learned

2015· article· en· W1565675407 sur OpenAlexaboutno aff
Tanya Coyle, Mai Ling Chan

Notice bibliographique

RevueASHA Leader · 2015
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueImpact of Technology on Adolescents
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAmerican Speech-Language-Hearing AssociationSocial mediaPsychologyMedical educationProfessional developmentPedagogyMedicineComputer scienceLinguisticsWorld Wide WebAsha

Résumé

récupéré en direct d'OpenAlex

You have accessThe ASHA LeaderGet Social1 Mar 2015Using Social Media for Professional Advancement: Lessons LearnedSee how peers use Twitter, Facebook, Instagram, Pinterest and more to improve clinical skills, interact with colleagues, share insights and discover the latest research. Tanya Coyle, andMSc, S-LP(C) Mai Ling ChanMS, SLP Tanya Coyle Google Scholar , MSc, S-LP(C) and Mai Ling Chan Google Scholar , MS, SLP https://doi.org/10.1044/leader.GS.20032015.np SectionsAbout ToolsAdd to favorites ShareFacebookTwitterLinked In If you’re interested in using social media professionally but haven’t been following the column so far, take a moment to catch up. Read on for professional social media 101: a roundup of previous articles written by active members of our socmed (social media) community. In our first column, we introduce you to what social media is and how some people conceptualize its use in the field. We liken it to one large and globally extending conference that never ends. We discuss how communicative disorders professionals are using various platforms such as Facebook, Twitter, Pinterest, blogs, and even Instagram. In case you want even more, there’s an article summarizing socmed platforms again and citing many ways in which to connect with ASHA and National Student Speech-Language Hearing Association by using them. The column also includes articles on how professionals incorporate social media into their practice. We advise that you don’t have to feel obliged to interact on these platforms and can benefit by just reading, although you’ll miss the opportunity for networking. And your peers already on social media are very willing to interact with you if you want. In addition, we share how professionals of all ages use social media and that there are ways to keep your personal life separate from your professional social media use. Now, let’s talk about a great trend in social media that we haven’t yet covered: Research Tuesday! Ian A. Elliott, a visiting assistant professor in the University of Massachusetts Lowell’s criminal justice department, first used #ResearchTues in November 2012. He reports he “had a backlog of journal alerts at that time” and “decided to set aside an hour every Tuesday to go through them and used #ResearchTues as a way to mentally keep myself on schedule.” Rachel Wynn, SLP and author/blogger of Gray Matter Therapy, began using this hashtag a year later and has since shaped this conversation into a more consistent and focused community. She invites bloggers and tweeters to “write about recent research that applies to their therapy practice” every second Tuesday of the month. This is well received in the communicative disorders twittersphere, as evidenced by the steady stream of tweets linking to research-relevant blogs and comments, as well as ASHA’s official adoption of the practice in December 2014. Specific details on how #ResearchTues works are available on Rachel’s blog. We hope you find this information helpful while you navigate available online communities. There’s more to come throughout this year. We will discuss how to use social media to find information, join conversations and share ideas about specific topics in speech pathology and audiology. Author Notes Tanya Coyle, MSc, S-LP(C), is a school-based SLP in Ontario, Canada who helped establish the speech-language community on Twitter in 2010. She has also authored speech-language and educational apps for iPad and is co-founder and director of social media for YappGuru Mai Ling Chan MS, SLP, is an Arizona based clinician, is co-founder and ceo of YappGuru where she provides consulting, training and collaboration to other industry experts to promote mobile technology integration into special education and rehabilitation. Advertising Disclaimer | Advertise With Us Advertising Disclaimer | Advertise With Us Additional Resources FiguresSourcesRelatedDetails Volume 20Issue 3March 2015 Get Permissions Add to your Mendeley library History Published in print: Mar 1, 2015 Metrics Current downloads: 322 Topicsasha-topicsleader_do_tagleader-topicsasha-article-typesCopyright & Permissions© 2015 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)0,022
score de la tête « metaresearch » (Gemma)0,030
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,115

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0220,030
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0040,005
Communication savante0,0110,022
Science ouverte0,0030,007
Intégrité de la recherche0,0070,012
Charge utile insuffisante (le modèle a refusé de juger)0,0130,007

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 Opus0,400
Tête enseignante GPT0,497
Écart entre enseignants0,097 · 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'étudeQualitatif
Domainenon disponible
GenreEmpirique

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
Publié2015
Routes d'admission1
Résumé présentoui

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