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Enregistrement W1964720701 · doi:10.1111/j.1365-2753.2009.01272.x

Understanding and changing Health Systems – an instinctive and natural process?

2009· editorial· en· W1964720701 sur OpenAlexaff
Carmel M. Martin

Notice bibliographique

RevueJournal of Evaluation in Clinical Practice · 2009
Typeeditorial
Langueen
DomaineHealth Professions
ThématiquePrimary Care and Health Outcomes
Établissements canadiensNOSM University
Organismes subventionnairesnon disponible
Mots-clésNegotiationComplex adaptive systemProcess (computing)Health careManagement scienceInstinctDysfunctional familyMental healthSociologyComputer sciencePsychologyRisk analysis (engineering)MedicinePolitical scienceEconomicsSocial scienceLawArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,024
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,024
Score d'incertitude au seuil0,125

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0240,025
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0080,134
Communication savante0,0200,036
Science ouverte0,0040,012
Intégrité de la recherche0,0060,008
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

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.

Tête enseignante Opus0,346
Tête enseignante GPT0,636
Écart entre enseignants0,290 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2009
Routes d'admission1
Résumé présentoui

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