Notice bibliographique
Résumé
It is through extreme examples or case studies that we can often acutely examine our own values, biases, paradigms, shortcomings or tendencies. In the commentary by Salvage, ‘Politics of Evidence: Conflict and Health in Iraq', there is an unequivocal demonstration that ‘truth is the first casualty of war’. But let's examine this further. Is it only during war that truth gets sacrificed? When do our own values, biases, political persuasions, etc. influence our interpretation and application of evidence? The ‘wars’ in healthcare are usually in the form of budget cuts, re-prioritisation, restructuring, downsizing, amalgamations, regionalisation and established power differentials, etc. It is during these healthcare wars that the value of clinical evidence appears to be most at risk. Who will protect and fight for externally validated evidence during these times? Most recently, there has been a wide propagation of evidence-based practice as a combined process involving systematically produced external evidence, clinical judgement, patient preferences and application context. Although this sounds intuitively and politically appropriate, what are the risks? Is there a risk of downplaying the utility of well-founded external evidence? Will this take us back to square one where we will continue to have wide variations of practices with a wide range of patient and system outcomes? It is clear we do not want evidence-based practice to be akin to a cookbook approach, as this would be disastrous; but, how do we balance between evidence and other aforementioned factors? What guidance can be provided to clinicians, administrators, policy makers, educators and researchers? For the nursing profession, however, it is an unprecedented opportunity to define rigorous processes regarding what to include and what not to include as evidence. The article ‘Comprehensive Systematic Review of Evidence on Developing and Sustaining Nursing Leadership That Fosters a Healthy Work Environment in Healthcare’ demonstrates that we do have room to define the inclusion of both quantitative and qualitative evidence as well as evidence from grey literature within a well-structured and rigorous process of critical appraisal and review of the evidence. And this process can lead to the development of applicable recommendations for practice (albeit in some instances cautious recommendations due to limitations in the availability of the evidence). Development and/or refinement of such processes require continual encouragement and debates such as those proposed by the authors of the article ‘Evidence-Based Decision Making: The Case for Diabetes Care’. Such ongoing debates will allow for a greater range of methodologies to be innovated and tested before the best ones are settled upon. Too early a closure on these debates may be harmful to the contributions that nursing brings in the arena of evidence-based practice. It is the purpose of editorial columns such as this to provoke responses and generally generate healthy discourse. Ask yourself, are we transitioning to Evidence-Informed Practice or are we still in the era of Evidence-Based Practice? Tazim Virani RN MScN Program Director, RNAO Nursing Best Practice Guidelines ProgramCo-Director, Nursing Best Practice Research UnitRNAO, Toronto, Ontario, Canada
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 | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,003 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 ».