Leading by virtual interaction: an application of cultural-historical activity theory
Bibliographic record
Abstract
Introduction The pandemic spread of SARS-CoV-2, a novel, highly contagious and easily transmissible pathogen, has profoundly affected all aspects of human interaction. Guided by the need to reduce face-to-face contacts, medical organisations have rapidly shifted group activities to virtual platforms. Over 1 year into the pandemic, the necessity to maintain public health restrictions ensures that virtual meetings will be the norm for the foreseeable future. It has yet to be understood how virtual technologies shape healthcare and academic cultures, affect interactions, or influence strategic decisions and policies within these systems. Conclusion In this article, the authors reflect on the move from historically situated activity systems of team leadership in healthcare to ones that now exist in virtual formats. Cultural-historical activity theory (CHAT) is a framework that explains complex human actions, and how they unfold over time through interaction with mediational tools (eg, technology) and various people representing their own communities, roles and perceived divisions of labour. The authors use the lens of CHAT as a framework to understand the shifting dynamics at play and offer strategies for leaders to co-establish activity systems with team members to make goals of group activities explicit and to deliberately work toward them. Five specific strategies proposed are: (1) use software platforms that fit your needs and give voice to all attendees with technical support present in meetings; (2) converse explicitly about roles and emerging role fluidity during times of change and pandemic response; (3) co-construct something new intentionally; (4) engage in implementation science at this time; and (5) lead intentionally while honouring cultural norms and values. It is imperative that any changes, even the ones that are a part of the pandemic response, are made consistent with the core shared values of the medical community as this necessary new way of coming together is embraced with collective wisdom.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".