Using Twitter (X) to Mobilize Knowledge for First Contact Physiotherapists: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Twitter (now X) is a digital social network commonly used by health care professionals. Little is known about whether it helps health care professionals to share, mobilize, and cocreate knowledge or reduce the time between research knowledge being created and used in clinical practice (the evidence-to-practice gap). Musculoskeletal first contact physiotherapists (FCPs) are primary care specialists who diagnose and treat people with musculoskeletal conditions without needing to see their general practitioner (family physician) first. They often work as a sole FCP in practice; hence, they are an ideal health care professional group with whom to explore knowledge mobilization using Twitter. OBJECTIVE: We aimed to explore how Twitter is and can be used to mobilize knowledge, including research findings, to inform FCPs' clinical practice. METHODS: Semistructured interviews of FCPs with experience of working in English primary care were conducted. FCPs were purposively sampled based on employment arrangements and Twitter use. Recruitment was accomplished via known FCP networks and Twitter, supplemented by snowball sampling. Interviews were conducted digitally and used a topic guide exploring FCP's perceptions and experiences of accessing knowledge, via Twitter, for clinical practice. Data were analyzed thematically and informed by the knowledge mobilization mindlines model. Public contributors were involved throughout. RESULTS: In total, 19 FCPs consented to the interview (Twitter users, n=14 and female, n=9). Three themes were identified: (1) How Twitter meets the needs of FCPs, (2) Twitter and a journey of knowledge to support clinical practice, and (3) factors impeding knowledge sharing on Twitter. FCPs described needs relating to isolated working practices, time demands, and role uncertainty. Twitter provided rapid access to succinct knowledge, the opportunity to network, and peer reassurance regarding clinical cases, evidence, and policy. FCPs took a journey of knowledge exchange on Twitter, including scrolling for knowledge, filtering for credibility and adapting knowledge for in-service training and clinical practice. Participants engaged best with images and infographics. FCPs described misinformation, bias, echo chambers, unprofessionalism, hostility, privacy concerns and blurred personal boundaries as factors impeding knowledge sharing on Twitter. Consequently, many did not feel confident enough to actively participate on Twitter. CONCLUSIONS: This study explores how Twitter is and can be used to mobilize knowledge to inform FCP clinical practice. Twitter can meet the knowledge needs of FCPs through rapid access to succinct knowledge, networking opportunities, and professional reassurance. The journey of knowledge exchange from Twitter to clinical practice can be explained by considering the mindlines model, which describes how FCPs exchange knowledge in digital and offline contexts. Findings demonstrate that Twitter can be a useful adjunct to FCP practice, although several factors impede knowledge sharing on the platform. We recommend social media training and enhanced governance guidance from professional bodies to support the use of Twitter for knowledge mobilization.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,007 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».