Is operational research true science? What should it be used for? [Editorial]
Notice bibliographique
Résumé
Public Health Action has now published over 100 articles focused on operational research. When we first launched the Journal, I told a colleague of mine, a researcher in clinical trials, about the journal and what we hoped to achieve with it. He turned and said to me ‘But that is not really science, is it?’ He is not alone in terms of his opinion about operational research. Clearly, this opinion must be taken seriously and addressed responsibly. What is science? According to the Oxford dictionary, it is ‘the systematic study of the structure and behaviour of the physical and natural world through observation and experiment’.1 Research in the life sciences begins with an observation leading to a (null) hypothesis which is tested using one of a set of standard study designs and evaluated using one of an approved set of statistical tests, based on a pre-determined level of probability. This provides the evidence upon which the appropriate form of standard care is determined for those seeking care. Operational research follows the same procedure, differing only from other types of research in selecting the operation of sys-tems and services as the focus of the research. This research addresses determinants of these operations and emphasizes the efficiency, accessibility, equity and quality of the services provided and evaluates the processes that contribute to improving these elements. The subject matter is that of the institutions and personnel providing the care as compared with the individual affected by the disease or condition, and often accesses routine records kept by the services to make the evaluations. Accordingly, operational research is as scientific as all other forms of research in the life sciences. PHA has published scientific articles on all manner of health services, primarily for the poor and from many places in the world. In this issue, for example, one study evaluated the predictors of development of tuberculosis disease in children living with HIV who were on isoniazid preventive treatment in Kenya.2 Another reported the additional value of fluorescence microscopy using light-emitting diode as compared with traditional smear microscopy in busy diagnostic laboratories in India.3 Still another determined, among TB patients offered HIV testing in India, who was most likely to accept the tests.4 Each of these studies provides information that will help services focus their practice more precisely and efficiently, refining approaches to those who are currently underserved. Challenges remain even in health services in the richest countries, as for example the poor quality of care provided in the Mid Staffordshire NHS Foundation Trust.5 This is a perfect example of where operational research can help to ensure that services continue to be of high quality, particularly where economic pressures are increasing. The benefit of operational research is only as good as the willingness to take up the results to improve the services. We look forward to publishing more research on the impact of operational research. The ultimate goal is not simply new knowledge, but a willingness to act on this new knowledge.
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 | 0,041 | 0,228 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,005 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,014 |
| Communication savante | 0,023 | 0,014 |
| Science ouverte | 0,006 | 0,002 |
| Intégrité de la recherche | 0,022 | 0,039 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,012 |
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 ».