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Record W4413215433 · doi:10.15173/jpc.v7i1.4489

Social Media in Pediatric Rehabilitation Research: Affordances of Facebook and Twitter for Knowledge Translation

2025· article· en· W4413215433 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of Professional Communication · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsAffordanceSocial mediaDialogicKnowledge translationStakeholderPublic relationsStakeholder engagementSociologyKnowledge managementInternet privacyPsychologyComputer scienceWorld Wide WebPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

This study examines the affordances of Facebook and Twitter as knowledge translation tools in pediatric rehabilitation and potentially other fields of research. Findings from content and discourse analyses of a private Facebook group and public Twitter account suggest that social media facilitates engagement and collaboration with stakeholders. Implementation of dialogic communication principles on Twitter increases the exposure, reach, and engagement of a network. Establishing an online community on Facebook develops a common understanding of issues, builds relationships and the promotes stakeholder involvement in research. By acknowledging the affordances of the two social media platforms, researchers can consider using Twitter for end-of-grant KT and Facebook groups for integrated knowledge translation. ©Journal of Professional Communication, all rights reserved.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.494
GPT teacher head0.584
Teacher spread0.090 · 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