Implementation of virtual academic detailing in North America: A qualitative study
Bibliographic record
Abstract
RATIONALE: The shift toward virtual academic detailing (AD) was accelerated by the COVID-19 pandemic. AIMS AND OBJECTIVES: We aimed to examine the role of external, contextual, and intrinsic programme-specific factors in virtual engagement of healthcare providers (HCPs) and delivery of AD. METHODS: AD groups throughout North America were contacted to participate in semistructured interviews. An interview guide was constructed by adapting the Consolidated Framework for Implementation Research (CFIR). A point of emphasis included strategies AD groups employed for provider engagement while implementing virtual AD programmes. Independent coders conducted qualitative analysis using the framework method. RESULTS: Fifteen AD groups from Canada (n = 3) and the United States (n = 12) participated. Technological issues and training detailers and HCPs were challenges during the transition to virtual AD visits. Restrictions on in-person activities during the pandemic created difficulties engaging HCPs and fewer AD visits. Continuing education was one strategy to incentivize participation, but credits were often not claimed by HCPs. Groups with established networks and prior experience with virtual AD leveraged connections to mitigate disruptions and continue AD visits. Other facilitators included emphasizing contemporary topics, including opioid education beyond fundamental guidelines. Virtual AD had the additional benefit of expanding geographic reach and flexible scheduling with providers. CONCLUSIONS: AD groups across North America have shifted to virtual outreach and delivery strategies. This trend toward virtual AD may aid outreach to vulnerable rural communities, improving health equity. More research is needed on the effectiveness of virtual AD and its future implications.
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.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".