Notice bibliographique
Résumé
The findings suggest that accountability is increasingly seen as important. This scrutiny has both positive and negative implications. It forces providers to be aware of what they are doing, and to strive to improve their performance. Many of our respondents told us that there is an increased focus on quality. Accountability has moved beyond its traditional focus on financial dimensions (and ensuring that resources are not misappropriated) to add a major focus on performance. These are all positive outcomes. Yet our findings also suggest some warnings that should be heeded to ensure these benefits are realized while minimizing adverse unintended consequences. One clear signal arises from the examination of accountability “to whom.” Across many subsectors, but particularly within hospitals and the community, multiple bodies ask for similar information, but in different forms. The resulting costs and confusion can be considerable. A number of the papers in this Special Issue note that informants are concerned that their efforts are being diverted to reporting at the expense of front-line care. A related issue is accountability “by whom.” Complying with accountability requests appears more difficult for smaller organizations, which may not have the resources to respond to increasing requests for information. Respondents begin to see some of these requests as make-work, particularly when they must duplicate efforts for few perceived additional benefits. To the extent that multiple parties are seeking accountability, it would seem advisable for them to coordinate their efforts. As noted in the introduction (Deber 2014), there are a variety of policy goals that can be pursued, among them access, quality (including safety), cost control/cost-effectiveness and customer satisfaction. The accountability models and tools used may focus on various combinations of these. The substudies suggest the importance of clarifying what should be done when conflicts arise among the demands, with different parties stressing different factors. Who should get priority and according to what criteria? Who will resolve conflicts? Do all these demanders of accountability have equal legitimacy? If not, how are they, or should they, be ranked? One clear example arose for professional self-regulation, where demands for transparency may conflict with demands to protect privacy. Another arises from the extent to which goals should be tailored to fit local circumstances. The UK example notes the tension between responding to local and national authorities. Similar tensions were evident in many of the substudies. Another clear finding comes from examining accountability “for what.” The findings from almost all of these substudies suggest the accuracy of our hypothesis, stated in the introduction, that the “production characteristics” of the goods and services being provided indeed have a major impact on accountability. In particular, measurability and controllability appear critical. This finding differs considerably by subsector. The quality of laboratory services, for example, is relatively simple to measure. In contrast, the quality of community-based care is much more difficult to capture in simple metrics, and the accountability metrics used incline heavily towards process measures. A major concern is that factors that are less easy to measure may be ignored, even if they are essential to success. A related concern is that organizations are reluctant to be held accountable for factors they cannot control. Public health units cannot control whether their population smokes, so may attempt to have such indicators removed from the metrics for which they will be held accountable. Cross-system issues showed up as a persistent omission; although ensuring smooth transitions among systems of care is a high priority, it did not turn up in most of the accountability measurement systems that were examined. A particular concern is that respondents in many of the substudies indicated the systems of accountability that had been set up often ignored many aspects they thought were important. This did not mean, however, that respondents thought accountability should be ignored. The bottom line is mixed. Accountability is important; taking steps towards it can help to ensure better-quality care and, ideally, both save resources and improve outcomes. Yet, poorly done, it can divert resources from crucial activities, erode support for what may seem like poorly conducted activities and miss the forest for the trees. We hope that these substudies can be helpful in highlighting strengths and avoiding potential weaknesses.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,002 |
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.
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 tête enseignante, 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 ».