Guidance for Implementing Video Consultations in Danish General Practice: Rapid Cycle Coproduction Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has changed various spheres of health care. General practitioners (GPs) have widely replaced face-to-face consultations with telephone or video consultations (VCs) to reduce the risk of COVID-19 transmission. Using VCs for health service delivery is an entirely new way of practicing for many GPs. However, this transition process has largely been conducted with no formal guidelines, which may have caused implementation barriers. This study presents a rapid cycle coproduction approach for developing a guide to assist VC implementation in general practice. OBJECTIVE: The aim of this paper is to describe the developmental phases of the VC guide to assist general practices in implementing VCs and summarize the evaluation made by general practice users. METHODS: The development of a guide for VC in general practice was structured as a stepped process based on the coproduction and prototyping processes. We used an iterative framework based on rapid qualitative analyses and interdisciplinary collaborations. Thus, the guide was developed in small, repeated cycles of development, implementation, evaluation, and adaptation, with a continuous exchange between research and practice. The data collection process was structured in 3 main phases. First, we conducted a literature review, recorded observations, and held informal and semistructured interviews. Second, we facilitated coproduction with stakeholders through 4 workshops with GPs, a group interview with patient representatives, and individual revisions by GPs. Third, nationwide testing was conducted in 5 general practice clinics and was followed by an evaluation of the guide through interviews with GPs. RESULTS: A rapid cycle coproduction approach was used to explore the needs of general practice in connection with the implementation of VC and to develop useful, relevant, and easily understandable guiding materials. Our findings suggest that a guide for VCs should include advice and recommendations regarding the organization of VCs, the technical setup, the appropriate target groups, patients' use of VCs, the performance of VCs, and the arrangements for booking a VC. CONCLUSIONS: The combination of coproduction, prototyping, small iterations, and rapid data analysis is a suitable approach when contextually rich, hands-on guide materials are urgently needed. Moreover, this method could provide an efficient way of developing relevant guide materials for general practice to aid the implementation of new technology beyond the pandemic period.
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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 | 0.055 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 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".