Networks of Care: A Social Network Perspective of Distributed Multidisciplinary Care for People With Inflammatory Arthritis
Bibliographic record
Abstract
OBJECTIVE: To explore how multidisciplinary inflammatory arthritis (IA) care is accessed from the perspectives of people with IA and their health care network members. METHODS: In this phenomenological study, we used purposive sampling to recruit patients with IA for less than 5 years and age of more than 18 years who spoke English and reported two or more health care network members. We conducted one-to-one interviews with patients and their health care network members. Data were analysed using a social network perspective. RESULTS: We interviewed 14 patient participants and 19 health care network members comprising health care providers and informal caregivers. An overarching theme of whole person (holistic) IA care was identified, with the following two broad multifaceted subthemes: 1) connected networks and whole person care and 2) network disconnect and disrupted access to care. The first subtheme notes how access to health care providers and social support was fundamental to holistic care and how care was facilitated by communication pathways that promoted care. The second subtheme illustrates impediments to access, including appointment time pressures, inadequacies in communication delivery modes, and family physicians' unfamiliarity with rheumatology care. Inequities in care were also reported. CONCLUSION: Participants shared a goal of whole person care. Although health care networks included multiple disciplines, they did not always provide coordinated multidisciplinary care. Communication modes, linkages between network actors, and organizational structures governed the flow of information and resources through networks and influenced access to equitable whole person care. The development of health care system structures to support the flow of information and resource transfer is needed to promote network collaboration and equitable access to resources.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".