Insight from extreme healthcare contexts: co-leadership’s stability paradox
Bibliographic record
Abstract
PURPOSE: Despite its significant potential to bridge and integrate, co-leadership is known to be fragile - a graphic in an organigram that doesn't transmit reality or illustrate the paralysis of tensions. Moments of transition, including changes in co-leaders, render the arrangement particularly vulnerable. Yet, we know little about how to maintain the stability of co-leadership arrangements. This study aims to explore that shortcoming in the context of frequent co-leader transitions. DESIGN/METHODOLOGY/APPROACH: Data was collected during a longitudinal qualitative study of co-leadership - the sharing of a leadership role by two individuals - in a military healthcare organization, where a context of frequent personnel rotations gives rise to almost yearly leadership transition events. An inductive analysis of 32 semistructured interviews with tactical-level co-leaders revealed three main factors contributing to the stability of this model, which has been in place for over 20 years. FINDINGS: In this case, the stability of co-leadership is rooted in three elements. First, similar structural arrangements and traditions are widely practiced and accepted within the field. Second, there is a common understanding that roles and relationships are negotiated on a temporary basis. Third, patterns of distancing contribute to maintaining the existing state. ORIGINALITY/VALUE: The ongoing replacement of leaders in an established co-leadership structure made for a unique and extreme case of instability, revealing the paradox of stability: in this case, the stability of the arrangement derives from the instability of its membership and from a shared view of its inner workings as a temporary modus operandi.
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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.039 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".