Leading through the first wave of COVID: a Canadian action research study
Bibliographic record
Abstract
PURPOSE: This first phase of a three-phase action research project aims to define leadership practices that should be used during and after the pandemic to re-imagine and rebuild the health and social care system. Specifically, the objectives were to determine what effective leadership practices Canadian health leaders have used through the first wave of the COVID-19 pandemic, to explore how these differ from pre-crisis practices; and to identify what leadership practices might be leveraged to create the desired health and care systems of the future. DESIGN/METHODOLOGY/APPROACH: The authors used an action research methodology. In the first phase, reported here, the authors conducted one-on-one, virtual interviews with 18 health leaders from across Canada and across leadership roles. Data were analyzed using grounded theory methodology. FINDINGS: Five key practices emerged from the data, within the core dimension of disrupting entrenched structures and leadership practices. These were, namely, responding to more complex emotions in self and others. Future practice identified to create more psychologically supportive workplaces. Agile and adaptive leadership. Future practice should allow leaders to move systemic change forward more quickly. Integrating diverse perspectives, within and across organizations, leveling hierarchies through bringing together a variety of perspectives in the decision-making process and engaging people more broadly in the co-creation of strategies. Applying existing leadership capabilities and experience. Future practice should develop and expand mentorship to support early career leadership. Communication was increased to build credibility and trust in response to changing and often contradictory emerging evidence and messaging. Future practice should increase communication. RESEARCH LIMITATIONS/IMPLICATIONS: The project was limited to health leaders in Canada and did not represent all provinces/territories. Participants were recruited through the leadership networks, while diverse, were not demographically representative. All interviews were conducted in English; in the second phase of the study, the authors will recruit a larger and more diverse sample and conduct interviews in both English and French. As the interviews took place during the early stages of the pandemic, it may be that health leaders' views of what may be required to re-define future health systems may change as the crisis shifts over time. PRACTICAL IMPLICATIONS: The sponsoring organization of this research - the Canadian Health Leadership Network and each of its individual member partners - will mobilize knowledge from this research, and subsequent phases, to inform processes for leadership development and, succession planning across, the Canadian health system, particularly those attributes unique to a context of crisis management but also necessary in post-crisis recovery. SOCIAL IMPLICATIONS: This research has shown that there is an immediate need to develop innovative and influential leadership action - commensurate with its findings - to supporting the evolution of the Canadian health system, the emotional well-being of the health-care workforce, the mental health of the population and challenges inherent in structural inequities across health and health care that discriminate against certain populations. ORIGINALITY/VALUE: An interdisciplinary group of health researchers and decision-makers from across Canada who came together rapidly to examine leadership practices during COVID-19's first wave using action research study design.
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.047 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| 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".