Understanding and changing Health Systems – an instinctive and natural process?
Notice bibliographique
Résumé
Currently, health research and health policy aim to make improvements in complex health systems, some of which have become overly stable or dysfunctional and are not meeting the needs of patients, families, communities or populations. In the face of paradox and differences in perspective from one group of agents or sectors to another in health systems, confidence in the ability of conventional categories to capture what is at stake and the processes for negotiating agreements has been eroded 1. Is this because we, as humans are innately unable to deal with complexity, or because our mental models and frameworks need to change? In this forum, we focus on the theory and application of a complex adaptive systems lens to real world health systems to mitigate their challenges and apparent paradoxes, particularly in relation to the implementation of change. Félix-Bortolotti provides a theoretical analysis of the concepts of primary health care and primary care and their assumptions by disentangling both concepts as well as their conceptual and empirical ramifications 2. Her paper makes the case that there are inherent contradictions built into the assumptions of both concepts and in the conditions under which they are constructed. It provides a theoretical and operational starting place for understanding the paradox of primary care 3, which plagues many policy makers, and sometimes even practitioners deeply embedded in the system. A primary health care (PHC) complexity framework is an important analytical tool to interrogate the ways in which the phenomenon of PHC is socially constructed, as well as in the matrices in which it is embedded. Also, the framework assists in the deconstruction of prevalent linear thinking built around PHC as a whole. Norman 4 argues that health promotion is a form of systems thinking and action, based on an analysis of the literature. He proposes innovative strategies and tools to enable health promoters to use complexity science effectively to inform systems change efforts. This is done by adequately comprehending the nature of system dynamics promote health in order to make sense of action in many if not most spheres. Norman 4 concludes that Complexity science is a vehicle for change as much as it is one for explaining how change may occur. Biswas et al. 5 present their work and efforts to understand and implement change in the real world of primary care and primary health care. How do we, on the basis of sense-making, navigate our way to better health for all in a complex system that feed back on themselves and each other? That is, do adaptation and evolution occur as they move forward in time and space 1? Biswas and colleagues ponder the vexing question: why despite many decades of good intentions, primary care and primary health care have not been introduced to India en masse 5? Major access barriers to basic health care, exist for the urban, and the rural and remote, and the poor and disadvantaged, and yet the current medical and health care systems remain ultra-stable, and focussed on specialized care. How can we perturb the system and shift its equilibrium to be more adaptive to population needs in order to address health inequalities. User driven information technologies and social networking may be the disruptive innovation to democratize and transform health care at a population level and an individual level. On a different theme, Sturmberg and Cilliers 6 reflect that modern society's preoccupation with time-efficiency has not been lost on the health care system. While time efficiency as a value is a result partly of design and partly of necessity, sufficient time, i.e. ‘a certain slowness’, is an essential element of the healing relationship in the consultation. Having the time to know the patient ultimately enables the clinician to hear the patient narrative and participate in co-constructing meaning of the health, illness and disease, rather than being limited to a simplified process of diagnosis and mechanical treatment. A more complete understanding of the patient health experience and their situation will not only lead to more effective treatment, but perhaps more efficient use of expensive health care resources. These results are important not only for the patient, but also for the doctor and the health system. The papers presented in this third Forum on Systems and Complexity form a coherent perspective, despite a range of different perspectives on the nature of health systems. They signpost a common direction in order to navigate and, when necessary, reconfigure stable and unstable components of adaptive dynamic systems 7. In reality we have no linear yard stick or formulae by which to distinguish a simple or a complicated system from a complex health system, nor to predict the tipping point of transition from one state to another. Yet, the human mind has a somewhat underutilized capacity to judge and gauge systems patterns and dynamics 8, particularly in their intimate environment. Humans naturally work in relationships and networks. Arguably, with the right models and with sufficient space for action and flexible constraints, we, as decision makers, academics, providers or health care users, can collectively recognize the domains of complexity and order, and sense phase transitions when they occur, and therefore make change with appropriate ‘slowness’9.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,134 |
| Communication savante | 0,020 | 0,036 |
| Science ouverte | 0,004 | 0,012 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».