L’avenir de la résolution des conflits dans le contexte de l’adoption de nouvelles technologies dans le domaine de la santé (The Future of Conflict Resolution in the Context of the Adoption of New Technologies in the Field of Health)
Bibliographic record
Abstract
Dans cet article, la professeure Regis propose que l’avenir de la resolution des conflits dans le contexte de l’adoption de nouvelles technologies repose sur notre capacite d’anticiper et de minimiser, autant que possible, les conflits. Autrement dit, il faut developper une mentalite de «prevention» plutot que de «resolution». Elle se sert de l’exemple de l’informatisation du reseau de sante au Quebec (Dossier sante Quebec) pour illustrer la relation entre les conflits, l’atteinte des objectifs de l’entente contractuelle et le manque de discussion du processus collaboratif entre les parties.Elle argumente que l’une des meilleures options pour passer a une mentalite de prevention est d’instaurer davantage de partenariats preventifs dans les projets de soins de sante, plus particulierement dans le cadre de projets complexes ou lorsque les risques sont incertains. Le partenariat preventif est un mecanisme de prevention des conflits dont l’objectif est de developper et de maintenir un processus collaboratif entre les parties pendant la duree d’un projet. Ce mecanisme necessite la mise en place de strategies solides de communication, la definition d’options flexibles et equitables de partage des risques ainsi que le monitoring des relations entre les parties. Alors que le partenariat preventif est bien etabli dans certains secteurs d’activites tels que la construction et l’ingenierie, il demeure sous-utilize dans le domaine de la sante. La professeure Regis explique ce que constitue le partenariat preventif, les possibilites et les defis que comporte un tel mecanisme ainsi que sa portee juridique.In this paper, professor Regis will argue that the future of conflict resolution in the adoption of new technologies lies in our capacity to minimize conflict, that is, to develop a new mind-set towards “prevention” instead of “resolution”. She will use the example of the computerization of the health care network in Quebec (Dossier sante Quebec) to illustrate the interrelation between conflict, achieving the goals of the contractual agreement, and the failure to discuss the collaboration process. She will submit that one of the best options to shift towards prevention is to further implement Partnering schemes in health care projects, especially in the context of complex projects or when risks fluctuate or are not well known. Partnering is a dispute prevention mechanism that aims at building and keeping a collaboration process among stakeholders and employees during the course of a project. It involves putting solid communication strategies in place, defining flexible and fair risk-sharing options and monitoring the evolution of the relationships. While this prevention mechanism is well established in some sectors such as construction and engineering, it remains under-utilized in health care. Dr. Regis will explain what partnering is, what potential and challenges such a dispute resolution mechanism holds as well as its legal value.
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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 | 0.055 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.036 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".