Beyond dyadic communication: Network of communication in inflammatory arthritis teams
Bibliographic record
Abstract
Following diagnosis, individuals with chronic conditions such as inflammatory arthritis (IA) face learning how to manage multiple new healthcare relationships. Despite existing literature on team care for people with IA, little is known about how teams negotiate care from perspectives of all team members. Objective To explore how communication is perceived and care is negotiated amongst IA healthcare teams by drawing on the perspectives of each team member. Method This analysis drew on data from an ongoing three-year study exploring team-based IA care. We interviewed 11 participants including two men with IA and their family care providers and healthcare providers. We used a three-staged analytic process and integrated broad tenets of social network theory to understand the relational dimensions of team members experiences. Result Analysis revealed three themes regarding communication and care: (1) seeking/sharing information, (2) striving to coordinate unified care, and (3) providing patients a voice. Discussion This study emphasizes the importance of understanding team dynamics beyond the dyad of patient and care provider. Negotiating power and decision-making in IA care is a dynamic process involving shifting levels of responsibility amongst a care team. Communication-based strategies that extend dyadic interactions may enhance teamwork and health outcomes in chronic conditions.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".