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Enregistrement W2016592211 · doi:10.1258/jhsrp.2010.009158

Response to ‘The Appropriation of Complexity Theory in Health Care’

2010· letter· en· W2016592211 sur OpenAlexaff
Trisha Greenhalgh, Paul E. Plsek, Sarah Fraser, Tim Holt

Notice bibliographique

RevueJournal of Health Services Research & Policy · 2010
Typeletter
Langueen
DomaineDecision Sciences
ThématiqueComplex Systems and Decision Making
Établissements canadiensPricewaterhouseCoopers (Canada)
Organismes subventionnairesNational Institute for Health and Care Research
Mots-clésFallacyEpistemologyAppropriationRhetorical questionSentenceField (mathematics)RigourArgumentation theoryAdversarySociologyPsychologyPhilosophyLinguisticsComputer scienceMathematics

Résumé

récupéré en direct d'OpenAlex

John Paley’s article is good teaching material because it illustrates some classic rhetorical moves used in the ancient sport of argumentation. These include the ‘post hoc ergo propter hoc’ fallacy (assuming that if B occurred after A then A must have caused B); the ‘slippery slope’ fallacy (suggesting that the opponent is guilty of A, then taking the audience through a series of small steps in which A becomes B, B becomes C etc, and concluding that the opponent is guilty of Z); the ‘ad hominem’ fallacy (depicting the opponent as ignorant or foolish, and then concluding that everything s/he says is weak and superficial); and the ‘ad populum’ fallacy (appealing to some positive but ill-defined quality to which the audience is assumed to aspire – such as ‘rigour’ or ‘depth of understanding’). It is ironic, for example, that in the very first sentence of a paper which purports to explain complexity theory, Paley draws a direct and linear link between a series of introductory articles which we published back in 2001 and alleged misunderstandings and misapplications of complexity theory by others in the health care field. Paley claims that in our 2001 series, we ‘partially understood’ what he chose to define as the second principle of complexity – that ‘successive states of the system, globally defined, are determined by previous states, locally defined’. We apparently ‘failed to recognize’ what Paley decided was complexity theory’s first principle – that complexity is an explanatory concept. It is presumably coincidental, then, that Paley chose to use the same example (a termite colony) to illustrate this principle as we ourselves used to illustrate it in the first article in our series. Paley’s third principle of complexity is expressed thus: ‘Complexity explanations account for global order by specifying the local behaviour of units which have no awareness of the order thereby being produced, and which have no intention to produce it’. If, as he claims, we ‘failed to recognize’ this, why did we say in our first article ‘Order, innovation, and progress can emerge naturally from the interactions within a complex system; they do not need to be imposed centrally or from outside. For example, termite colonies construct the highest structures on the planet relative to the size of the builders. Yet there is no chief executive termite, no architect termite, and no blueprint. Each individual termite acts locally, seemingly following only a few simple shared rules of behaviour, within a context of other termites also acting locally. The termite mound emerges from a process of self organization’? Paley suggests that in our article on complexity, leadership and management, we misinterpreted the notion of the self-organizing system to mean that bottom-up approaches to organizational strategy and development should replace top-down ones. Had this been true, it would have been a grievous fault. What Plsek and Wilson actually said was ‘Complexity based organizational thinking suggests that goals and resources are established with a view towards the whole system, rather than artificially allocating them to parts of the system’. The article, written at a time when National Service Frameworks and other rigid, nationally imposed performance targets were stifling local initiative and flexibility, was arguing that tight central control can be counterproductive, not that organizations work better with nobody in charge. The analysis of an argument is incomplete without a consideration of the audience for which it was Trisha Greenhalgh MD, Professor of Primary Health Care, University College London, 206 Holborn Union Building, Highgate Hill, London N19 5LW, UK; Paul Plsek MSc, Independent consultant, Paul E. Plsek & Associates, Inc., Atlanta, USA; Tim Wilson FRCGP, Partner, PricewaterhouseCoopers, London, UK; Sarah Fraser DProf, Director, Sarah Fraser & Associates Ltd, Aylesbury, UK; Tim Holt FRCGP, Clinical Lecturer, University of Warwick, Coventry, UK.

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)0,078
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: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,039
Score d'incertitude au seuil0,127

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0240,078
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0090,045
Communication savante0,0100,017
Science ouverte0,0040,013
Intégrité de la recherche0,0390,078
Charge utile insuffisante (le modèle a refusé de juger)0,0100,002

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 Opus0,289
Tête enseignante GPT0,559
Écart entre enseignants0,270 · 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
GenreCommentaire

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

En bref

Citations49
Publié2010
Routes d'admission1
Résumé présentoui

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