Challenges and success strategies for dyad leadership model in healthcare
Bibliographic record
Abstract
The use of a dyad leadership model involving a physician co-leader and a co-leader with a different background, the dyad co-leader, is gradually increasing in Healthcare Organizations (HCOs). There is a paucity of empirical studies on various aspects of this model. This study's aim was to identify challenges and strategies for success in the dyad leadership model in healthcare. Through a mixed-methods approach utilizing focus groups, surveys, and semi-structured interviews, perceptions of 37 leaders in one HCO at different hierarchical levels were analysed based on their lived experiences. The challenges and success strategies spanned personal, interpersonal, and organizational domains. The areas requiring attention included mindsets, competencies, interpersonal relationship, support, time, communication, and collaboration. In addition, the importance of organizational context addressing its structure, strategy, operations, and culture was highlighted. The findings from this study may be used for praxis, development, and implementation of dyad leadership.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".