Using Twitter (X) to Mobilize Knowledge for First Contact Physiotherapists: Qualitative Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".