The relevance of the LEADS framework during the COVID-19 pandemic
Bibliographic record
Abstract
COVID-19 has created a unique context for the practice of leadership in healthcare. Given the significant use of the LEADS in a Caring Environment capabilities framework (LEADS) in Canada's health system, it is important to document the relevancy of LEADS. The authors reviewed literature, conducted research, and reflected on their own experience to identify leadership practices during the pandemic and related them to LEADS. Findings are presented in three sections: Hindsight (before), Insight (during), and Foresight (post). We profile the issue of improving long-term Care to provide an example of how LEADS can be applied in crisis times. Our analysis suggests that while LEADS appears to specify the leadership capabilities needed, it requires adaptation to context. The vision Canada has for healthcare will dictate how LEADS will be used as a guide to leadership practice in the current context or to shape a bolder vision of healthcare's future.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.039 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".