Change agency in a primary health care context
Bibliographic record
Abstract
BACKGROUND: Integration of services across disciplines and organizations has been pursued increasingly in the primary care sector. Successful integration requires adept leadership of change. There have been questions about the extent to which studies on change agency that focus on a stand-alone leader are applicable in the complex setting of health care. It has been suggested that a model of collective leadership is more appropriate to this setting. PURPOSE: The objective is to understand the dynamics of collective or distributed leadership by attending to change agency roles in a context involving collaboration across health organizations. The study examines how change agency roles develop, evolve, interact, and complement each other. It also examines the bases of the change agents' ability to exercise influence. METHODOLOGY: A qualitative, longitudinal case study allowed us to map the evolution of a successful model of leadership. We tracked changes and agents' roles by engaging in extensive observations and conducting 74 interviews over a period of 4 years. FINDINGS: The findings point to the importance of the distributed change leadership model in contexts where legitimacy, authority, resources, and ability to influence complex change are dispersed across loci. Distributed leadership has both planned and emergent components, and its success in bringing about change is associated with the social capital prevalent in the site. PRACTICE IMPLICATIONS: Change leaders need to build a winning coalition of agents with complementary skills and resources that support the change. Successful change leadership involves investing time in finding common ground across stakeholders and in building credibility and trust. Having an agent whose main responsibility is to manage the change process is likely to bring more success than asking busy health care practitioners to take on this charge because in the latter case, there is likelihood of dilution of change focus and momentum.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".