Promoting Coevolution Between Healthcare Organizations and Communities as Part of Social and Health Pathways Management in Quebec: Contributions of the Complex Adaptive Systems Approach
Bibliographic record
Abstract
The implementation of sociosanitary pathways in the Quebec healthcare system aims to better meet the needs of communities and strengthen their participation at all levels of governance. This initiative will form the basis of our article, which will look at the challenges of adaptation both inside and outside organizations. Drawing on the complex adaptive systems approach, we have developed an analytical framework to highlight the processes that can lead to the adaptation of governance to facilitate community participation in the management of this pathways. The aim of this article is to propose a better understanding of coevolution in the process(es) of adaption of the governance of a complex healthcare organization to its environment, by mobilizing the complex adaptive systems approach. We conducted a qualitative case study, based on 4 sources: documents (n = 70) produced or used during implementation, participatory observations on various tactical and operational committees of the management structure, collaborative workshops with members of the management committee, and semi-structured interviews (n = 18) with managers, department heads, partners, and users of health and social services. To understand the co-evolutionary processes involved in the implementation of management by social and health pathways, we present our results in response to 3 research proposals on the theme of internal and external coherence in a healthcare organization, in terms of vision (cultural), structures (organizational and clinical), and relationships with external partners (environment). Our findings show that to implement and manage an innovation in a healthcare organization, it is fundamental to foster coevolution at operational, tactical and strategic levels, as well as with the external environment. To achieve this, it is necessary to maintain a balance and internal coherence between the structure being implemented and the existing structure, to establish formal and informal communication channels to ensure seamless interactions, while recognizing and reinforcing mutual interdependence in a systemic perspective.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".